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High prevalence of hypertension and complicated medication knowledge have presented challenges to hypertension clinicians and general practitioners. Clinical decision support systems (CDSSs) are developed to aid clinicians in decision making. Current clinical knowledge is stored in fixed templates, which are not intuitive for clinicians and limit the knowledge reusability. Knowledge graphs (KGs) store knowledge in a way that is not only intuitive to humans but also processable by computers directly. However, existing medical KGs such as UMLS and CMeKG are general purpose and thus lack enough knowledge to enable hypertension medication.We first construct a KG specific to hypertension medication according to the Chinese hypertension guideline and then develop the corresponding CDSS to implement hypertension medication and knowledge management. Current advances in knowledge graph representation and modelling are researched and applied in the complex medical knowledge representation. Traditional knowledge representation and KG representation are innovatively combined in the storage of the KG to enable convenient knowledge management and easy application by the CDSS. Along a predefined reasoning path in the KG, the CDSS finally accomplishes the hypertension medication by applying knowledge stored in the KG. 124 health records of a hypertension Chief Physician from Beijing Anzhen Hospital, Capital Medical University, are collected to evaluate the system metrics on the single drug recommendation task.The proposed CDSS has functions of medication knowledge graph management and hypertension medication decision support. With elaborate design on knowledge representation, knowledge management is intuitive and convenient. By virtue of the KG, medication recommendations are highly visualized and explainable. Experiments on 124 health records with 90% guideline compliance collected from hospitals in single class recommendation task achieve 91%, 83% and 77% on recall, hit@3 and MRR metrics respectively, which demonstrates the quality of the KG and effectiveness of the system.

Background and Objective The leading cause of vision loss in the Western World is Age-related Macular Degeneration (AMD), but together with modern medicines, tracking the number of Hyperreflective Foci (HF) on Optical Coherence Tomography (OCT) images should assist the treatment of patients. Here, we developed a framework based on deep learning for the automatic segmentation of HF in OCT images. Methods We collected OCT images and annotated them, then these images underwent image preprocessing, and feature extraction steps. Using the prepared data we trained different types of Conventional-, Deep- and Convolutional Neural Networks to perform the task of the automatic segmentation of HF. Results We evaluated the various Neural Networks, by performing HF segmentation of clinical data belonging to patients, whose data were excluded from the training process. The results suggest that our systems can achieve reasonably high Dice Coefficient values, and they are comparable with (i.e., in most cases above 95%) the similarity between manual annotations performed by different physicians. Conclusion From the results, it can be concluded that neural networks can be used to accurately segment HF in OCT images. The results are sufficiently accurate for us to incorporate them into the next phase of the research, building a decision support system for everyday clinical practice.

Background and Objective Fraction of Inspired Oxygen is one of the arbitrary set ventilator parameters which has critical influence on the concentration of blood oxygen. Normally mechanical ventilators providing respiratory assistance are tuned manually to supply required inspired oxygen to keep the oxygen saturation at the desired level. Maintaining oxygen saturation in the desired limit is so vital since excess supply of inspired oxygen leads to hypercapnia and respiratory acidosis which lead to increased risk in cell damage and death. On the other side a sudden drop in oxygen saturation will lead to severe cardiac arrest and seizure. Hence intelligent real time control of blood oxygen level saturation is highly significant for patients in intensive care units. Methods This paper gives statistical pair wise analysis for finding out deeply correlated physiological parameters from clinical data for fixing fuzzy variables. An advisory fuzzy controller using Mamdani model is developed with R programming to predict FiO2 which is to be delivered from the ventilator to maintain SaO2 with in required levels. Results Fuzzy variables for the fuzzy model is fixed using 75% of the clinical data collected. Remaining 25% of the data is used for checking the system. Compared the predictive output of the system with physicians’ decisions and found to be accurate with less than five percentage error. Conclusions Based on the comparison the system is proved to be effective and can be used as assist mode for physicians for effective decision making.

Background and objective Hospitals already acquire a large amount of data, mainly for administrative, billing and registration purposes. Tapping on these already available data for additional purposes, aiming at improving care, without significant incremental effort and cost. This potential of secondary patient data is explored through modeling administrative and billing data, as well as the hierarchical structure of pathology codes of the International Classification of Diseases (ICD) in the prediction of unplanned readmissions, as a clinically relevant outcome parameter that can be impacted on in a quality improvement program. Methods In this single-center, hospital-wide observational cohort study, we included all adult patients discharged in 2016 after applying an exclusion protocol (n = 29,702). In addition to administrative variables, such as age and length of stay, structured pathology data were taken into account in predictive models. As a first research question, we compared logistic regression against penalized logistic regression, gradient boosting and Random Forests to predict unplanned readmission. As a second research goal, we investigated the level of hierarchy within the pathology data needed to achieve the best accuracy. Finally, we investigated which prediction variables play a prominent role in predicting hospital readmission. The performance of all models was evaluated using the Area Under the ROC Curve (AUC) measure. Results All models have the best predictive results using Random Forests. An added value of 7% is observed compared to a baseline method such as logistic regression. The best model, based on Random Forests, achieved an AUC of 0.77, using the diagnosis category and procedure code as lowest level of the hierarchical pathology data. Conclusions The most accurate model to predict hospital wide unplanned readmission is based on Random Forests and includes the ICD hierarchy, especially diagnosis category. Such an approach lowers the number of predictor variables and yields a higher interpretability than a model based on a detailed diagnosis. The performance of the model proved high enough to be used as a decision support tool.

A Clinical Decision Support System (CDSS) aims to assist physicians, nurses and other professionals in decision-making related to the patient’s clinical condition. CDSSs deal with pertinent and critical data, and special care should be taken in their design to ensure the development of usable, secure and reliable tools. Objective: This paper aims to investigate existing literature dealing with the development process of CDSSs for monitoring chronic diseases, analysing their functionalities and characteristics, and the software engineering representation in their design. Methods: A systematic literature review (SLR) is conducted to analyse the literature on CDSSs for monitoring chronic diseases and the application of software engineering techniques in their design. Results: Fourteen included studies revealed that the most addressed disease was diabetes (42.8%) and the most commonly proposed approach was diagnostic (85.7%). Regarding data sources, the studies show a predominance on the use of databases (85.7%), with other data sources such as sensors (42.8%) and self-report (28.6%) also being considered. Analysing the representation for engineering techniques, we found Behaviour diagrams (42.8%) to be the most frequent, closely followed by Structural diagrams (35.7%) and others (78.6%) being largely mentioned. Some studies also approached the requirement specification (21.4%). The most common target evaluation was the performance of the system (64.2%) and the most common metric was accuracy (57.1%). Conclusion: We conclude that software engineering, in its completeness, has scarce representation in studies focused on the development of CDSSs for chronic diseases.

Background and objective In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes. Method The experiments were conducted from a set of 8756 medical records with 50 different features about diabetic readmission. After preprocessing the data, an effective SMOTE-based method was proposed to solve the imbalance data problem. Further, in order to improve prediction performance, a hybrid feature selection mechanism was devised to select the important features. Subsequently, an improved support vector machine-based (SVM-based) method was developed and the genetic algorithm was used to tune the sensitive parameter of the algorithm. Finally, the five-fold cross-validation method was applied to compare the performance of proposed method with other methods (LACE score, logistic regression, naive bayes, decision tree and feed forward neural networks). Results Experimental results indicate that the proposed SVM-based method achieves an accuracy of 81.02%, a sensitivity of 82.89%, a specificity of 79.23%, and outperforms other popular algorithms in identifying diabetic patients who may be readmitted. Conclusions Our research can improve the performance of clinic decision support systems for diabetic readmission, by which the readmission possibility as well as the waste of medical resources can be reduced.

Background and objective: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy. Methods: This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data. Results: The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively. Conclusions: The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

Objective and background: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructured data. The success of smart healthcare applications such as clinical decision support systems, disease diagnosis systems, and healthcare management systems depends on knowledge that is understandable by machines to interpret and infer new knowledge from it. In this regard, ontological data models are expected to play a vital role to organize, integrate, and make informative inferences with the knowledge implicit in that unstructured data and represent the resultant knowledge in a form that machines can understand. However, constructing such models is challenging because they demand intensive labor, domain experts, and ontology engineers. Such requirements impose a limit on the scale or scope of ontological data models. We present a framework that will allow mitigating the time-intensity to build ontologies and achieve machine interoperability. Methods: Empowered by linked biomedical ontologies, our proposed novel Automated Ontology Generation Framework consists of five major modules: a) Text Processing using compute on demand approach. b) Medical Semantic Annotation using N-Gram, ontology linking and classification algorithms, c) Relation Extraction using graph method and Syntactic Patterns, d), Semantic Enrichment using RDF mining, e) Domain Inference Engine to build the formal ontology. Results: Quantitative evaluations show 84.78% recall, 53.35% precision, and 67.70% F-measure in terms of disease-drug concepts identification; 85.51% recall, 69.61% precision, and F-measure 76.74% with respect to taxonomic relation extraction; and 77.20% recall, 40.10% precision, and F-measure 52.78% with respect to biomedical non-taxonomic relation extraction. Conclusion: We present an automated ontology generation framework that is empowered by Linked Biomedical Ontologies. This framework integrates various natural language processing, semantic enrichment, syntactic pattern, and graph algorithm based techniques. Moreover, it shows that using Linked Biomedical Ontologies enables a promising solution to the problem of automating the process of disease-drug ontology generation.

Background and objective Musculoskeletal disorders (MSDs) are one of the most important causes of disability with a high prevalence. The accurate and timely diagnosis of these disorders is often difficult. Clinical decision support systems (CDSSs) can help physicians to diagnose diseases quickly and accurately. Given the ambiguous nature of MSDs, fuzzy logic can be helpful in designing the CDSSs knowledge bases. The present study aimed to review the studies on fuzzy CDSSs to diagnose MSDs. Methods A comprehensive search was conducted in Medline, Scopus, Cochrane Library, and ISI Web of Science databases to identify relevant studies published until March 15, 2016. Studies were included in which CDSSs were developed using fuzzy logic to diagnose MSDs, and tested their accuracy using real data from patients. Results Of the 3188 papers examined, 23 papers included according to the inclusion criteria. The results showed that among all the designed CDSSs only one (CADIAG-2) was implemented in the clinical environment. In about half of the included studies (52%), CDSSs were designed to diagnose inflammatory/infectious disorder of the bone and joint. In most of the included studies (70%), the knowledge was extracted using a combination of three methods (acquiring from experts, analyzing the data, and reviewing the literature). The median accuracy of fuzzy rule-based CDSSs was 91% and it was 90% for other fuzzy models. The most frequently used membership functions were triangular and trapezoidal functions, and the most used method for inference was the Mamdani. Conclusions In general, fuzzy CDSSs have a high accuracy to diagnose MSDs. Despite the high accuracy, these systems have been used to a limited extent in the clinical environments. To design of knowledge base for CDSSs to diagnose MSDs, rule-based methods are used more than other fuzzy methods.

Background and Objective Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices. Methods Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis. Results Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected. Conclusions Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.

Background and Objective Early detection of cardiovascular (CV) disease or associated risk factors during childhood is of paramount importance, allowing for early treatment or lifestyle modifications, respectively. The objective of this study was to describe the development of an electronic health record (EHR), with integrated computerized decision support system (CDSS), specifically designed for supporting the needs of a pilot pediatric CV disease screening program applied on primary school students of a Mediterranean island. Methods Evidence-based knowledge, national and international practice guidelines regarding sport preparticipation CV screening of children and young athletes has been used for the design of the designated EHR. A CDSS, capable for providing alerts for further cardiology evaluation need, has been incorporated into the EHR, based on normative anthropometric and electrocardiographic data as well as predefined positive history responses. Results We developed a designated EHR with integrated CDSS supporting pediatric CV disease screening, capable for documenting CV-related personal and family history responses, physical evaluation data (weight, height, blood pressure), allowing for entering electrocardiogam (ECG) measurements and for uploading of multimedia files (including ECG images and digital phonocardiogram audio files). The EHR incorporates clinical calculators and referral alerts for the presence (and degree) of adiposity, hypertension, ECG abnormalities and positive history responses indicative of high CV disease risk. In a preliminary EHR validation, performed by entering data from 53 previously available paper-based health records, the EHR was proven to be fully functional. Conclusions The pediatric cardiology EHR with CDSS features which we developed might serve as a model for EHR for primary health care purposes, capable to document and early detect CV disease and associated risk factors in pediatric populations.

Background: In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge. Objective: The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations. Methods: In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested. Results: A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1–7 weeks, whereas CPGs recommend 2–4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1–6 weeks, and 2–5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3–6 month range. Conclusions: Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints’ representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).

Background: Clinical decision support systems (CDSSs) are developed to support healthcare practitioners with decision-making about therapy and diagnosis’ confirmation, among others. Although there are many advantages of using CDSSs, there are still many challenges in their adoption. Therefore, it is essential to ensure the quality of the system, so that it can be used confidently and securely. Objective: This study aims to propose a set of (sub)characteristics which should be considered in evaluating the quality-in-use of CDSSs, based on the ISO/IEC 25010 standard and on existing literature. Methods: We reviewed the existing literature on CDSS assessment and presented a list of quality characteristics evaluated. Results: Ten quality characteristics and 56 sub-characteristics were identified and selected from the literature, in which usability was evaluated the most. An example of a scenario has been presented to illustrate our assessment approach of satisfaction and efficiency as important quality-in-use characteristics to be applied in the evaluation of a CDSS. Conclusion: The proposed approach will contribute in bridging the gap between the quality of CDSSs and their adoption.

Accepted manuscript version. Published version available in Computer Methods and Programs in Biomedicine 2017, 152:105-114. Objectives: Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records using data-driven clinical decision support tools. However, these tools depend on labeled training data and can be both time consuming and expensive to create. Methods: The recent learning with anchors framework resolves this problem by transforming key observations (anchors) into labels. This is a promising framework, but it is heavily reliant on clinicians knowledge for specifying good anchor choices in order to perform well. In this paper we propose a novel method for specifying anchors from free text documents, following an exploratory data analysis approach based on clustering and data visualization techniques. We investigate the use of the new framework as a way to detect postoperative delirium. Results: By applying the proposed method to medical data gathered from a Norwegian university hospital, we increase the area under the precision-recall curve from 0.51 to 0.96 compared to baselines. Conclusions: The proposed approach can be used as a framework for clinical decision support for postoperative delirium

Objective Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows. Our proposed system provides a user-friendly authoring environment to create Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making by CDSS. Methods and materials Existing systems are not physician friendly and lack interoperability and shareability of knowledge. In this paper, we proposed Intelligent-Knowledge Authoring Tool (I-KAT), a knowledge authoring environment that overcomes the above mentioned limitations. Shareability is achieved by creating a knowledge base from MLMs using Arden Syntax. Interoperability is enhanced using standard data models and terminologies. However, creation of shareable and interoperable knowledge using Arden Syntax without abstraction increases complexity, which ultimately makes it difficult for physicians to use the authoring environment. Therefore, physician friendliness is provided by abstraction at the application layer to reduce complexity. This abstraction is regulated by mappings created between legacy system concepts, which are modeled as domain clinical model (DCM) and decision support standards such as virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We represent these mappings with a semantic reconciliation model (SRM). Results The objective of the study is the creation of shareable and interoperable knowledge using a user-friendly and flexible I-KAT. Therefore we evaluated our system using completeness and user satisfaction criteria, which we assessed through the system- and user-centric evaluation processes. For system-centric evaluation, we compared the implementation of clinical information modelling system requirements in our proposed system and in existing systems. The results suggested that 82.05% of the requirements were fully supported, 7.69% were partially supported, and 10.25% were not supported by our system. In the existing systems, 35.89% of requirements were fully supported, 28.20% were partially supported, and 35.89% were not supported. For user-centric evaluation, the assessment criterion was ‘ease of use’. Our proposed system showed 15 times better results with respect to MLM creation time than the existing systems. Moreover, on average, the participants made only one error in MLM creation using our proposed system, but 13 errors per MLM using the existing systems. Conclusion We provide a user-friendly authoring environment for creation of shareable and interoperable knowledge for CDSS to overcome knowledge acquisition complexity. The authoring environment uses state-of-the-art decision support-related clinical standards with increased ease of use.

This study examines the reliability of the clinical use of the decision support system.The study collected data based on 107 domestic subjects, who were divided into two groups.The two groups then underwent TOVA and DS-ADHD testing.The survey and testing results underwent the one-way ANOVA and the split-half method statistical analysis.The study results show that the system has the characteristics of screening for ADHD, based on its reliability and validity. Background and objectivesThe purpose of this study is to examine the reliability of the clinical use of the self-built decision support system, diagnosis-supported attention deficit hyperactivity disorder (DS-ADHD), in an effort to develop the DS-ADHD system, by probing into the development of indicating patterns of past screening support systems for ADHD. MethodsThe study collected data based on 107 subjects, who were divided into two groups, non-ADHD and ADHD, based on the doctor's determination, using the DSM-IV diagnostic standards. The two groups then underwent Test of Variables of Attention (TOVA) and DS-ADHD testing. The survey and testing results underwent one-way ANOVA and split-half method statistical analysis, in order to further understand whether there were any differences between the DS-ADHD and the identification tools used in today's clinical trials. ResultsThe results of the study are as follows: 1) The ROC area between the TOVA and the clinical identification rate is 0.787 (95% confidence interval: 0.7010.872); 2) The ROC area between the DS-ADHD and the clinical identification rate is 0.867 (95% confidence interval: 0.8010.933). ConclusionsThe study results show that DS-ADHD has the characteristics of screening for ADHD, based on its reliability and validity. It does not display any statistical differences when compared with TOVA systems that are currently on the market. However, the system is more effective and the accuracy rate is better than TOVA. It is a good tool to screen ADHD not only in Chinese children, but also in western country.

Background and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification. Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.

Background Mental disorders, according to the definition of World Health Organization, consist of a wide range of signs, which are generally specified by a combination of unusual thoughts, feelings, behavior, and relationships with others. Social anxiety disorder (SAD) is one of the most prevalent mental disorders, described as permanent and severe fear or feeling of embarrassment in social situations. Considering the imprecise nature of SAD symptoms, the main objective of this study was to generate an intelligent decision support system for SAD diagnosis, using Adaptive neuro-fuzzy inference system (ANFIS) technique and to conduct an evaluation method, using sensitivity, specificity and accuracy metrics. Method In this study, a real-world dataset with the sample size of 214 was selected and used to generate the model. The method comprised a multi-stage procedure named preprocessing, classification, and evaluation. The preprocessing stage, itself, consists of three steps called normalization, feature selection, and anomaly detection, using the Self-Organizing Map (SOM) clustering method. The ANFIS technique with 5-fold cross-validation was used for the classification of social anxiety disorder. Results and conclusion The preprocessed dataset with seven input features were used to train the ANFIS model. The hybrid optimization learning algorithm and 41 epochs were used as optimal learning parameters. The accuracy, sensitivity, and specificity metrics were reported 98.67%, 97.14%, and 100%, respectively. The results revealed that the proposed model was quite appropriate for SAD diagnosis and in line with findings of other studies. Further research study addressing the design of a decision support system for diagnosing the severity of SAD is recommended.

False positive alerts in CDSS incorrectly prompt when "no-risk" patients are encountered.A randomized controlled trial investigates physician responses toward false positive alerts.Physicians are not likely to adopt recommendations provided by false positive alerts in patient-safety-related CDSS.Reporting the adoption rate of CDSS as a whole without differentiation leads to an underestimation of system effectiveness. ObjectivesFalse positive alerts in patient-safety-related clinical decision support systems (CDSS) are defined as alerts which incorrectly prompt when no-risk patients are encountered. It is an unfavorable condition which may potentially mislead physicians. The aim is to investigate physician responses toward false positive (FP) and true positive (TP) alerts in CDSS for the prevention of contrast-induced nephropathy (CIN). MethodsA two-arm cluster randomized controlled trial was conducted in university hospitals. Eligible physicians were randomized to receive alert intervention or no intervention (groups 1 and 2, respectively). The alert system was embedded with a deliberately non-specific risk detection tool in order to generate TP and FP alerts. The naive alert system would alert the physician to cancel the order regardless of the patient being at-risk or not at-risk. CIN risk was stratified as at-risk and no-risk according to a patient's pre-existing renal function. Contrast imaging order-cancellation rate was measured as primary outcome. Results3802 contrast-enhanced examination orders from 66 physicians were analyzed. Demographic data and risk distributions of patients were similar and well-balanced between two groups. In the intervention group, a total of 1892 alerts were generated (332 TP alerts and 1560 FP alerts). Order-cancellation rates were 5.1% versus 1.4% in groups 1 and 2 for at-risk patients (relative risk RRź=ź3.69) from TP alerts, and 1.0% versus 1.4% for no-risk patients (RRź=ź0.71) from FP alerts. Using generalized linear model with generalized estimating equation, the FP alerts had no order-cancellation effect when compared to the control arm (adjusted RRź=ź0.69; 95%CI, 0.36-1.32). The TP alerts had a larger order-cancellation effect than that of the control arm (adjusted RRź=ź2.95; 95%CI, 0.94-9.27), which revealed a marginal trend toward significance. However, the effect was not statistically significant (adjusted RRź=ź1.24; 95%CI, 0.71-2.18) if TP and FP alerts were mixed. ConclusionsPhysicians are not likely to adopt recommendations provided by false positive alerts in patient-safety-related CDSS. If reporting only the adoption rate of CDSS as a whole without differentiating between TP and FP alerts, the effects of TP and FP alerts will be mixed, and thus, will lead to an underestimation of system effectiveness.

The main objective of this study was to investigate the impact of implementing a computer-based order entry system without clinical decision support on the number of radiographs ordered for patients seen in the emergency department.Our results show a decrease in the number of radiographs ordered after computer-based order entry system implementation, despite an increase in the number of emergency department admissions.Our study also shows that the time interval between emergency department admission and medical imaging was not affected by this new workflow. Background and objectiveThe adoption of computerized physician order entry is an important cornerstone of using health information technology (HIT) in health care. The transition from paper to computer forms presents a change in physicians' practices. The main objective of this study was to investigate the impact of implementing a computer-based order entry (CPOE) system without clinical decision support on the number of radiographs ordered for patients admitted in the emergency department. MethodsThis single-center pre-/post-intervention study was conducted in January, 2013 (before CPOE period) and January, 2014 (after CPOE period) at the emergency department at Nimes University Hospital. All patients admitted in the emergency department who had undergone medical imaging were included in the study. ResultsEmergency department admissions have increased since the implementation of CPOE (5388 in the period before CPOE implementation vs. 5808 patients after CPOE implementation, p=.008). In the period before CPOE implementation, 2345 patients (44%) had undergone medical imaging; in the period after CPOE implementation, 2306 patients (40%) had undergone medical imaging (p=.008). In the period before CPOE, 2916 medical imaging procedures were ordered; in the period after CPOE, 2876 medical imaging procedures were ordered (p=.006). In the period before CPOE, 1885 radiographs were ordered; in the period after CPOE, 1776 radiographs were ordered (p

Multifaceted aspects in time and time-oriented concepts.Comparison of clinical data models in handling time.Ontologies of representation and reasoning about time in the clinical domain.Constructing the timelines for the medical histories of patients.Temporal concept coreference resolution problem. Background and objectiveWe live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic health records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. MethodsThis review surveys the methods used in three important area: modeling and representing of time, medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. ResultsThe main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. ConclusionsExtracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

This study proposes a novel method to segment the tumor in the breast ultrasound images.The proposed approach combined the neutrosophic similarity score with level set algorithm for image segmentation.Neutrosophic similarity score is employed to deal with uncertain information on image segmentation. Breast ultrasound (BUS) image segmentation is a challenging task due to the speckle noise, poor quality of the ultrasound images and size and location of the breast lesions. In this paper, we propose a new BUS image segmentation algorithm based on neutrosophic similarity score (NSS) and level set algorithm. At first, the input BUS image is transferred to the NS domain via three membership subsets T, I and F, and then, a similarity score NSS is defined and employed to measure the belonging degree to the true tumor region. Finally, the level set method is used to segment the tumor from the background tissue region in the NSS image. Experiments have been conducted on a variety of clinical BUS images. Several measurements are used to evaluate and compare the proposed method's performance. The experimental results demonstrate that the proposed method is able to segment the BUS images effectively and accurately.

Background and Objective Effective sharing and reuse of Electronic Health Records (EHR) requires technological solutions which deal with different representations and different models of data. This includes information models, domain models and, ideally, inference models, which enable clinical decision support based on a knowledge base and facts. Our goal is to develop a framework to support EHR interoperability based on transformation and reasoning services intended for clinical data and knowledge. Methods Our framework is based on workflows whose primary components are reusable mappings. Key features are an integrated representation, storage, and exploitation of different types of mappings for clinical data transformation purposes, as well as the support for the discovery of new workflows. The current framework supports mappings which take advantage of the best features of EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Results We have implemented CLIN-IK-LINKS, a web-based platform that enables users to create, modify and delete mappings as well as to define and execute workflows. The platform has been applied in two use cases: semantic publishing of clinical laboratory test results; and implementation of two colorectal cancer screening protocols. Real data have been used in both use cases. Conclusions The CLIN-IK-LINKS platform allows the composition and execution of clinical data transformation workflows to convert EHR data into EHR and/or semantic web standards. Having proved its usefulness to implement clinical data transformation applications of interest, CLIN-IK-LINKS can be regarded as a valuable contribution to improve the semantic interoperability of EHR systems.

Highlights • Intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19. • ABO compatibility is assessed after classifying donors into the four blood types. • Contracted patient decision matrix in-between‘serological/protein biomarkers and the PaO2/FiO2’ and ‘patients list’. • Proposed a novel subjective and objective decision by opinion score method (SODOSM)’. • Contracted CP-decision matrix in-between serological/protein bio-markers criteria and ‘CPs tested list’., Context People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. Objective This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods. Method The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between ‘serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria’ and ‘patient list based on novel MCDM method known as subjective and objective decision by opinion score method’. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix. Result An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments. Discussion The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.

Objective Severe sepsis is a leading cause of intensive care unit (ICU) admission, length of stay, mortality, and cost. systemic inflammatory response syndrome (SIRS) and organ failure due to infection define it, but also make it hard to diagnose. Early diagnosis reduces morbidity, mortality and cost, and diagnosis is often significantly delayed due to a lack of effective biomarkers. This research employs kernel density estimation (KDE) methods fusing a personalized, model-based insulin sensitivity (SI) metric with standard bedside measures of: temperature, heart rate, respiratory rate, systolic and diastolic blood pressure, and SIRS, as these measures are available hourly or more frequently. Methods Model-based SI is a derived metric, identified using clinical data and a clinically validated metabolic model. The KDE classifier discriminates severe sepsis and septic shock from moderate sepsis using accepted consensus sepsis scores. A best case in-sample estimate, a worst case independent cross validation estimate, and an accepted .632 bootstrap estimate are calculated to assess performance using multi-level likelihood ratios, and sensitivity and specificity. Performance is assessed against clinically and statistically defined thresholds denoted for the minimum acceptable level as: “high accuracy, often providing useful information, and clinical significance,” and similar definitions for greater or lesser quality. Results The .632 bootstrap estimate performs near clinically defined levels of high accuracy, often providing useful information, and clinical significance based on sensitivity, specificity, and multilevel likelihood ratios. Conclusion and significance The classifier created and this overall approach is useful for clinical decision making in diagnosing severe sepsis and septic shock in real time, for both case and control hours. However, improvements could be made with larger clinical data sets.

Background and objective To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. Methods We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) developed. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned outcomes. A confusion matrix was computed. Results A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. Conclusions Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.

Objectives Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. Methods We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians’ manual review for appropriateness. Results One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80–96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. Conclusion We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.

Background and Objective Given the phenomenon of aging population, dementias arise as a complex health problem throughout the world. Several methods of machine learning have been applied to the task of predicting dementias. Given its diagnostic complexity, the great challenge lies in distinguishing patients with some type of dementia from healthy people. Particularly in the early stages, the diagnosis positively impacts the quality of life of both the patient and the family. This work presents a hybrid data mining model, involving the mining of texts integrated to the mining of structured data. This model aims to assist specialists in the diagnosis of patients with clinical suspicion of dementia. Methods The experiments were conducted from a set of 605 medical records with 19 different attributes about patients with cognitive decline reports. Firstly, a new structured attribute was created from a text mining process. It was the result of clustering the patient's pathological history information stored in an unstructured textual attribute. Classification algorithms (naive bayes, bayesian belief networks and decision trees) were applied to obtain Alzheimer's disease and mild cognitive impairment predictive models. Ensemble methods (Bagging, Boosting and Random Forests) were used in order to improve the accuracy of the generated models. These methods were applied in two datasets: one containing only the original structured data; the other containing the original structured data with the inclusion of the new attribute resulting from the text mining (hybrid model). Results The models’ accuracy metrics obtained from the two different datasets were compared. The results evidenced the greater effectiveness of the hybrid model in the diagnostic prediction for the pathologies of interest. Conclusions When analysing the different methods of classification and clustering used, the better rates related to the precision and sensitivity of the pathologies under study were obtained with hybrid models with support of ensemble methods.

Background and Objective Urinary tract infection (UTI) is a common disease affecting the vast majority of people. UTI involves a simple infection caused by urinary tract inflammation as well as a complicated infection that may be caused by an inflammation of other urinary tract organs. Since all of these infections have similar symptoms, it is difficult to identify the cause of primary infection. Therefore, it is not easy to diagnose a UTI with routine examination procedures. Invasive methods that require surgery could be necessary. This study aims to develop an artificial intelligence model to support the diagnosis of UTI with complex symptoms. Methods Firstly, routine examination data and definitive diagnosis results for 59 UTI patients gathered and composed as a UTI dataset. Three classification models namely; decision tree (DT), support vector machine (SVM), random forest (RF) and artificial neural network (ANN), which are widely used in medical diagnosis systems, were created to model the definitive diagnosis results using the composed UTI dataset. Accuracy, specificity and sensitivity statistical measurements were used to determine the performance of created models. Results DT, SVM, RF and ANN models have 93.22%, 96.61%, 96.61%, 98.30% accuracy, 95.55%, 97.77%, 95.55%, 97.77% sensitivity and 85.71%, 92.85%, 100%, 100% specificy results, respectively. Conclusions ANN has the highest accuracy result of 98.3% for UTI diagnosis within the proposed models. Although several symptoms, laboratory findings, and ultrasound results are needed for clinical UTI diagnosis, this ANN model only needs pollacuria, suprapubic pain symptoms and erythrocyte to get the same diagnosis with such accuracy. This proposed model is a successful medical decision support system for UTI with complex symptoms. Usage of this artificial intelligence method has its advantages of lower diagnosis cost, lower diagnosis time and there is no need for invasive methods.

Background and Objective A framework of sparse deformation prediction using Markove Decision Processes is proposed for achieving a rapid and accurate registration by providing a suitable initial deformation. Methods In the proposed framework, the tree is built based on the training set for each patch from the template image. The template patch is considered as the root. The node is the patch group in which multiple similar patches are extracted around a key point on the training image. Given the linkages between patch groups in the tree, MDP is introduced to select the optimal path with highest registration accuracy from each training patch to the template patch. The deformation between them is estimated along the selected path by patch-wise registration which can be realized by a non-learning-based method. Given the patches on a testing image, their best matching patches are fast chosen from the training patches and the corresponding deformations constitute a sparse deformation. A dense deformation for the entire test image is subsequently interpolated and used as an initial deformation for further registration. Results With the non-learning-based registration as the baseline method, the proposed framework is evaluated using three datasets of inter-subject brain MR images with three learning-based methods. Experimental results of the non-learning-based method using the proposed framework reveal that the computation time is reduced by fivefold after using the proposed framework. And, with the same baseline method, the proposed framework demonstrates the higher accuracy than three learning-based methods which predicts the initial deformation at image scale. The mean Dice of three datasets for the tissues of the brain are 73.52%, 70.73% and 64.82%, respectively. Conclusions The proposed framework rapidly registers the inter-subject brains and achieves the high mean Dice for the tissues of the brain.

We look for an approach to massive risk factor discovery for early childhood caries.Association rule mining with adequate rule pruning and ranking performs well.Literature supports most risk factors identified in associative analysis.When coupled with other risk factors frequent breastfeeding confirmed as a risk.Parent health awareness significant only for boys. Background and objectiveEarly childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. MethodsECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Backa area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. ResultsDiscovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. ConclusionsThe discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods.

Medical calculators synthesize measurable evidence and help introduce new medical guidelines and standards. Some medical calculators can fulfill the role of CDS for Meaningful Use purposes. However, there are barriers for clinicians to use medical calculators in practice. Objectives of this study were to determine whether lack of EHR integration would be a barrier to use of medical calculators, and understand factors that may limit use and perceived usefulness of calculators METHODS: A survey about medical calculators as they relate to clinical efficiency, perceived usefulness, and barriers to effective use was conducted at a medium-sized academic medical center. 819 physicians were invited to participate in an online survey with a 13% response rate. Results were statistically analyzed to highlight factors related to use or non-use of medical calculators.We found a negative correlation between use of medical calculators and years of experience (p 0.001), with decreasing calculator use as experience goes up. Barriers to using medical calculators by non-users and users of medical calculators show that necessity and integration are significantly different with p 0.001 and p = 0.037, respectively. 46.7% of non-users reported necessity as a barrier compared to 7.7% of users. Integration was reported as a barrier for 43.6% of users, but only 13.3% of non-users. 61% of users indicated that calculators made them more efficient, and 70% reported that unavailability of normally used calculators make them less efficient. 60% of users indicated that they are somewhat or very likely to use newly published medical calculators.The results highlight that medical calculators are important for care delivery by both users and non-users. For non-users, they are seen as having a potentially positive impact on patient care, but unnecessary as part of clinical practice. For medical calculator users, calculators are an important part of regular workflow for efficiency improvement. Clinicians with fewer years of experience show an eagerness to consume newly published calculators, making these kinds of CDS a potentially useful way to disseminate new medical evidence. The survey results suggest that when medical calculators can be automated and integrated into the EHR as part of everyday workflow then efficiency and adoption may increase.

HighlightsWe design and built a decision support system for the treatment of breast cancer based on Asbru.The DSS is integrated with the electronic patient record in use in a Medical Oncology Unit.We evaluated the DSS in the multidisciplinary meeting for the decision of the adjuvant treatment.The results show an agreement of nearly 90% between the meeting decisions and the DSS recommendations.We discuss the reasons for non-agreement and additional insights into the design of effective DSS. The domain of cancer treatment is a promising field for the implementation and evaluation of a protocol-based clinical decision support system, because of the algorithmic nature of treatment recommendations. However, many factors can limit such systems' potential to support the decision of clinicians: technical challenges related to the interoperability with existing electronic patient records and clinical challenges related to the inherent complexity of the decisions, often collectively taken by panels of different specialists. In this paper, we evaluate the performances of an Asbru-based decision support system implementing treatment protocols for breast cancer, which accesses data from an oncological electronic patient record. Focusing on the decision on the adjuvant pharmaceutical treatment for patients affected by early invasive breast cancer, we evaluate the matching of the system's recommendations with those issued by the multidisciplinary panel held weekly in a hospital.

This model is to introduce a new concept called Mean Prescription Rank of prescriptions.The physicians will able to select relevant medications even in the case of inappropriate (unintentional) selection.To enhance efficiency of the e-prescribing system. BackgroundThe report from the Institute of Medicine, To Err Is Human: Building a Safer Health System in 1999 drew a special attention towards preventable medical errors and patient safety. The American Reinvestment and Recovery Act of 2009 and federal criteria of 'Meaningful use' stage 1 mandated e-prescribing to be used by eligible providers in order to access Medicaid and Medicare incentive payments. Inappropriate prescribing has been identified as a preventable cause of at least 20% of drug-related adverse events. A few studies reported system-related errors and have offered targeted recommendations on improving and enhancing e-prescribing system. ObjectiveThis study aims to enhance efficiency of the e-prescribing system by shortening the medication list, reducing the risk of inappropriate selection of medication, as well as in reducing the prescribing time of physicians. Method103.48 million prescriptions from Taiwan's national health insurance claim data were used to compute Diagnosis-Medication association. Furthermore, 100,000 prescriptions were randomly selected to develop a smart medication recommendation model by using association rules of data mining. Results and conclusionThe important contribution of this model is to introduce a new concept called Mean Prescription Rank (MPR) of prescriptions and Coverage Rate (CR) of prescriptions. A proactive medication list (PML) was computed using MPR and CR. With this model the medication drop-down menu is significantly shortened, thereby reducing medication selection errors and prescription times. The physicians will still select relevant medications even in the case of inappropriate (unintentional) selection.

Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods.