14 results on '"Clinical decision support system (CDSS)"'
Search Results
2. Design and implementation of an automatic nursing assessment system based on CDSS technology.
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Dai, Ling, Wu, Zhijun, Pan, Xiaocheng, Zheng, Dingchang, Kang, Mengli, Zhou, Mingming, Chen, Guanyu, Liu, Haipeng, and Tian, Xin
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- 2024
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3. Predicting lab values for gastrointestinal bleeding patients in the intensive care unit: A comparative study on the impact of comorbidities and medications.
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Mahani, Golnar K. and Pajoohan, Mohammad-Reza
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COMORBIDITY , *INTENSIVE care patients - Abstract
Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. By considering the issues of MIMIC-II in the process of data extraction and using expert knowledge, comprehensive preprocessing has been performed to validate the data. The first prediction system is developed using zero order Takagi-Sugeno fuzzy modeling and the sequential forward selection method. The results of this prediction system for target laboratory tests are promising. In the second proposed prediction system, patients are clustered using their comorbidity information before the final prediction phase. For each cluster, a medication feature is constructed and added to the data for the final feature selection. Although it was expected that clustering patients based on comorbidity data could improve the results of value prediction, the results were not improved in average. The reason for this could be the small number of abnormal laboratory test samples and their dispersion in clusters. These abnormal values would be more dispersed in the model with clustering phase, when they are scattered over different clusters. [ABSTRACT FROM AUTHOR] more...
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- 2019
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4. Effects of chemotherapy prescription clinical decision-support systems on the chemotherapy process: A systematic review.
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Rahimi, Rezvan, Moghaddasi, Hamid, Rafsanjani, Khadijeh Arjmandi, Bahoush, Gholamreza, and Kazemi, Alireza
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Objective: To carry out a systematic review of studies assessing the effects of chemotherapy prescription clinical decision-support systems (CDSSs) on the chemotherapy process.Methods: Articles published in English before May 1, 2017 and indexed in the PubMed and Embase databases were reviewed systematically. Studies that focused on the effects of chemotherapy prescription CDSSs on the chemotherapy process were included in this research and reviewed.Results: 2283 articles were retrieved, of which 37 met the inclusion criteria. Twenty-seven of the included studies reported the effect of chemotherapy prescription CDSSs on medication errors, 18 studies focused on user satisfaction and system acceptance, 10 articles studied the effect of CDSSs on costs and care time and only 3 studies examined the impact on compliance with chemotherapy protocols.Discussion and Conclusion: In most of the studies, the use of CDSSs in chemotherapy prescription has reduced medication errors, especially dosage errors and has also reduced the time of chemotherapy process takes. However, in a few studies, the system has not been effective in reducing medication errors, has increased certain type of errors or has introduced new errors. Most of the software used has been specifically designed for the chemotherapy process and is intended to increase user satisfaction and system acceptance. There was not sufficient evidence on the effects of these systems on compliance with protocols and chemotherapy costs to draw firm conclusion. Higher quality studies are required to provide more evidence on the effects of CDSSs on medication errors, user satisfaction and system acceptance, costs, care time and compliance with protocols. [ABSTRACT FROM AUTHOR] more...- Published
- 2019
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5. Predicting successful placements for youth in child welfare with machine learning.
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Trudeau, Kimberlee J., Yang, Jichen, Di, Jiaming, Lu, Yi, and Kraus, David R.
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WELL-being , *PILOT projects , *MACHINE learning , *CHILD welfare , *PREDICTION models - Abstract
• Machine Learning (ML) uses historical data to make future predictions. • We developed and validated ML models to predict treatment success per placement. • Guidelines for use of Clinical Decision Support Systems with youth are discussed. Out-of-home placement decisions have extremely high stakes for the present and future well-being of children in care because some placement types, and multiple placements, are associated with poor outcomes. We propose that a clinical decision support system (CDSS) using existing data about children and their previous placement success could inform future placement decision-making for their peers. The objective of this study was to test the feasibility of developing machine learning models to predict the best level of care placement (i.e., the placement with the highest likelihood of doing well in treatment) based on each youth's behavioral health needs and characteristics. We developed machine learning models to predict the probability of each youth's treatment success in psychiatric residential care (i.e., Psychiatric Residential Treatment Facility [PRTF]) versus any other placement (AUROCs > 0.70) using data collected in standard care at a behavioral health organization. Placement recommendations based on these machine learning models distinguished between youth who did well in residential care versus non-residential care (e.g., 80% of those who received care in the recommended setting with the highest predicted likelihood of success had above average risk-adjusted outcomes). Then we developed and validated machine learning models to predict the probability of each youth's treatment success across specific placement types in a state-wide system, achieving an average AUROC score of >0.75. Machine learning models based on risk-adjusted behavioral health and functional data show promise in predicting positive placement outcomes and informing future placement decisions for youth in care. Related ethical considerations are discussed. [ABSTRACT FROM AUTHOR] more...
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- 2023
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6. Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique.
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Wajid, Summrina Kanwal, Hussain, Amir, and Huang, Kaizhu
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HISTOGRAMS , *BREAST cancer , *MAGNETIC resonance imaging , *VOLUME (Cubic content) , *MACHINE learning - Abstract
In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an intelligent expert system that can aid medical practitioners making diagnostic decisions. Analysis of volumetric images, slice by slice, is cumbersome and inefficient. Hence, 3D-LESH is designed to compute a histogram-based feature set from a local energy map, calculated using a phase congruency (PC) measure of volumetric Magnetic Resonance Imaging (MRI) scans in 3D space. 3D-LESH features are invariant to contrast intensity variations within different slices of the MRI scan and are thus suitable for medical image analysis. The contribution of this article is manifold. First, we formulate a novel 3D-LESH feature extraction technique for 3D medical images to analyse volumetric images. Further, the proposed 3D-LESH algorithmis, for the first time, applied to medical MRI images. The final contribution is the design of an intelligent clinical decision support system (CDSS) as a multi-stage approach, combining novel 3D-LESH feature extraction with machine learning classifiers, to detect cancer from breast MRI scans. The proposed system applies contrast-limited adaptive histogram equalisation (CLAHE) to the MRI images before extracting 3D-LESH features. Furthermore, a selected subset of these features is fed into a machine-learning classifier, namely, a support vector machine (SVM), an extreme learning machine (ELM) or an echo state network (ESN) classifier, to detect abnormalities and distinguish between different stages of abnormality. We demonstrate the performance of the proposed technique by its application to benchmark breast cancer MRI images. The results indicate high-performance accuracy of the proposed system (98%±0.0050, with an area under a receiver operating charactertistic curve value of 0.9900 ± 0.0050) with multiple classifiers. When compared with the state-of-the-art wavelet-based feature extraction technique, statistical analysis provides conclusive evidence of the significance of our proposed 3D-LESH algorithm. [ABSTRACT FROM AUTHOR] more...
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- 2018
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7. Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients.
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Zenziper Straichman, Yael, Kurnik, Daniel, Matok, Ilan, Halkin, Hillel, Markovits, Noa, Ziv, Amitai, Shamiss, Ari, and Loebstein, Ronen
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MEDICAL decision making , *HOSPITAL patients , *DRUG prescribing , *MEDICATION errors , *SOCIAL support , *MEDICATION error prevention , *DECISION support systems , *DRUG interactions , *INFORMATION storage & retrieval systems , *MEDICAL databases , *LONGITUDINAL method , *MEDICAL prescriptions , *PHYSICIANS , *RETROSPECTIVE studies - Abstract
Background: Clinical decision support systems (CDSS) reduce prescription errors, but their effectiveness is reduced by high alert rates, "alert fatigue", and indiscriminate rejection.old>Objectives: To compare acceptance rates of alerts generated by the SafeRx® prescription CDSS among different alert types and departments in a tertiary care hospital, identify factors associated with alert acceptance, and determine whether alert overrides were justified.Methods: In a retrospective study, we compared acceptance rates of all prescription alerts generated in 2013 in 18 departments of Israel's largest tertiary care center. In a prospective study in 2 internal medicine departments, we collected data on factors potentially associated with alert override, and an expert panel evaluated the justification for each overridden alert. We used multivariate analyses to examine the association between patient and physician-related factors and alert acceptance.Results: In the retrospective study, of 390,841 prescriptions, 37.1% triggered at least one alert, 5.3% of which were accepted. Acceptance rates ranged from 7.9% for excessive dose alerts to 4.0% for duplicate drug and major drug-drug interactions alerts (p<0.001). In the prospective study, common reasons for alert overriding included "irrelevance to the specific condition" and "medication previously tolerated by the patient". Weekend shifts (incident rate ratio [IRR]=1.50 [95% CI, 1.01-2.22]) and a specific department (IRR=1.87 [1.23-2.87]) were associated with higher alert acceptance, while night shift (IRR=0.47 [0.26-0.85]) was associated with alert override. Most alert overrides (88.6%) were judged justified.Conclusions: The vast majority of SafeRx® alerts are overridden, and overriding is justified in most cases. Minimizing the number of alerts is essential to reduce the likelihood of developing "alert fatigue". Our findings may inform a rational, department-specific approach for alert silencing. [ABSTRACT FROM AUTHOR] more...- Published
- 2017
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8. An encoding methodology for medical knowledge using SNOMED CT ontology.
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El-Sappagh, Shaker and Elmogy, Mohammed
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SYSTEMATIZED Nomenclature of Medicine ,SUBJECT headings ,ONTOLOGY ,DIAGNOSIS of diabetes ,CASE-based reasoning ,PROBLEM solving - Abstract
Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semantically-intelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an OWL2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre-coordination concept coverage of ∼75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases. [ABSTRACT FROM AUTHOR] more...
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- 2016
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9. AHP based Classification Algorithm Selection for Clinical Decision Support System Development.
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Khanmohammadi, Sina and Rezaeiahari, Mandana
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ANALYTIC hierarchy process ,CLASSIFICATION algorithms ,DECISION support systems ,SUPERVISED learning ,DATA analysis - Abstract
Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use different algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based meta-learning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS). The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets. [ABSTRACT FROM AUTHOR] more...
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- 2014
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10. Guidelines for maternal and neonatal “point of care”: Needs of and attitudes towards a computerized clinical decision support system in rural Burkina Faso.
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Zakane, S Alphonse, Gustafsson, Lars L, Tomson, Göran, Loukanova, Svetla, Sié, Ali, Nasiell, Josefine, and Bastholm-Rahmner, Pia
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DECISION support systems , *MEDICAL care , *INFORMATION technology , *PRENATAL care - Abstract
Highlights: [•] Healthcare workers in rural Africa have a great willingness to adapt and use modern technologies. [•] Guidelines through CDSS in maternal care are perceived as a learning tool. [•] Users’ adoption behaviour to CDSS can be divided into motivators and barriers. [•] Aspects of motivators and barriers are closely connected. [•] Motivating aspects can easily be turned into barriers if not taken care of properly in the final design of the CDSS. [ABSTRACT FROM AUTHOR] more...
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- 2014
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11. An end stage kidney disease predictor based on an artificial neural networks ensemble
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Di Noia, Tommaso, Ostuni, Vito Claudio, Pesce, Francesco, Binetti, Giulio, Naso, David, Schena, Francesco Paolo, and Di Sciascio, Eugenio
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ARTIFICIAL neural networks , *IGA glomerulonephritis , *CHRONIC kidney failure , *PHYSICIANS , *CLASSIFICATION - Abstract
Abstract: IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement therapy with dialysis or kidney transplantation. The need for new tools able to help clinicians in predicting ESKD risk for IgAN patients is highly recognized in the medical field. In this paper we present a software tool that exploits the power of artificial neural networks to classify patients’ health status potentially leading to ESKD. The classifier leverages the results returned by an ensemble of 10 networks trained by using data collected in a period of 38years at University of Bari. The developed tool has been made available both as an online Web application and as an Android mobile app. Noteworthy to its clinical usefulness is that its development is based on the largest available cohort worldwide. [Copyright &y& Elsevier] more...
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- 2013
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12. Impact of four training conditions on physician use of a web-based clinical decision support system.
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Kealey, Edith, Leckman-Westin, Emily, and Finnerty, Molly T.
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DECISION support systems , *PHYSICIAN training , *WEB-based user interfaces , *FOLLOW-up studies (Medicine) , *COHORT analysis , *RETROSPECTIVE studies , *SCIENTIFIC observation - Abstract
Abstract: Background: Training has been identified as an important barrier to implementation of clinical decision support systems (CDSSs), but little is known about the effectiveness of different training approaches. Methods: Using an observational retrospective cohort design, we examined the impact of four training conditions on physician use of a CDSS: (1) computer lab training with individualized follow-up (CL-FU) (n =40), (2) computer lab training without follow-up (CL) (n =177), (3) lecture demonstration (LD) (n =16), or (4) no training (NT) (n =134). Odds ratios of any use and ongoing use under training conditions were compared to no training over a 2-year follow-up period. Results: CL-FU was associated with the highest percent of active users and odds for any use (90.0%, odds ratio (OR)=10.2, 95% confidence interval (CI): 3.2–32.9) and ongoing use (60.0%, OR=6.1 95% CI: 2.6–13.7), followed by CL (any use=81.4%, OR=5.3, CI: 2.9–9.6; ongoing use=28.8%, OR=1.7, 95% CI: 1.0–3.0). LD was not superior to no training (any use=47%, ongoing use=22.4%). Conclusion: Training format may have differential effects on initial and long-term follow-up of CDSSs use by physicians. [Copyright &y& Elsevier] more...
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- 2013
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13. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules.
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Anooj, P.K.
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DECISION support systems ,HEART disease diagnosis ,MACHINE learning ,CARDIAC patients ,DATA mining ,ARTIFICIAL neural networks ,FUZZY algorithms - Abstract
Abstract: As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer-based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts’ opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient’s clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity. [Copyright &y& Elsevier] more...
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- 2012
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14. AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction
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Eom, Jae-Hong, Kim, Sung-Chun, and Zhang, Byoung-Tak
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CARDIOVASCULAR diseases , *DIAGNOSIS , *CLINICAL medicine , *DECISION support systems - Abstract
Abstract: Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation. [Copyright &y& Elsevier] more...
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- 2008
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