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This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.

The administration of blood products is a common, resource-intensive, and potentially problem-prone area that may place patients at elevated risk in the clinical setting. Much of the emphasis in transfusion safety has been targeted toward quality control measures in laboratory settings where blood products are prepared for administration as well as in automation of certain laboratory processes. In contrast, the process of transfusing blood in the clinical setting (ie, at the point of care) has essentially remained unchanged over the past several decades. Many of the currently available methods for improving the quality and safety of blood transfusions in the clinical setting rely on informal process descriptions, such as flow charts and medical algorithms, to describe medical processes. These informal descriptions, although useful in presenting an overview of standard processes, can be ambiguous or incomplete. For example, they often describe only the standard process and leave out how to handle possible failures or exceptions. One alternative to these informal descriptions is to use formal process definitions, which can serve as the basis for a variety of analyses because these formal definitions offer precision in the representation of all possible ways that a process can be carried out in both standard and exceptional situations. Formal process definitions have not previously been used to describe and improve medical processes. The use of such formal definitions to prospectively identify potential error and improve the transfusion process has not previously been reported. The purpose of this article is to introduce the concept of formally defining processes and to describe how formal definitions of blood transfusion processes can be used to detect and correct transfusion process errors in ways not currently possible using existing quality improvement methods.

1. Hassan A. Aziz, PhD, MLS(ASCP)cm[⇑][1] 1. College of Arts and Sciences, Qatar University, Doha – Qatar 2. Rebecca L. Bearden 1. College of Arts and Sciences, Qatar University, Doha – Qatar 3. Asha Elmi, MS 1. Weill Cornell Medical College – Qatar, Doha – Qatar 1. Address for Correspondence: Hassan A. Aziz, PhD, MLS(ASCP)cm, Associate Dean for Academic Affairs, Director and Associate Professor of Biomedical Science, College of Arts and Sciences, Qatar University, P.O. Box: 2713, Doha – Qatar, 00974-4403-4783, Hassan.Aziz{at}qu.edu.qa 1. Describe the patient-physician relationship. 2. Assess the current challenges of a healthy patient-physician relationship. 3. Evaluate the role of information technology in improving the relationship between patients and physicians. 4. Define the term clinical decision support system. 5. Discuss the benefits and challenges of clinical decision support systems. INTRODUCTION In the last quarter of the twentieth century rapid development in the biological, medical, and pharmaceutical sciences has taken place and is continually developing causing a considerable broadening and deepening in the medical knowledge and thus the emergence of new methods of examination and treatment of patients. There is high importance in the development of medical and technical sciences and as a result the amount of information needed by physicians in their practice for the diagnosis and treatment of various diseases has significantly increased. Using new and emerging drugs, diagnostics, and data from evidence-based medicine should help improve the clinical and cost effectiveness of care. Definition of Patient-Physician Relationship Physician-patient effective relationship is the main key to deliver good therapy and high quality healthcare in the medical field. Physicians and their communication, social and relational skills are the main pillars that help in collecting information and data from patients to facilitate precise and accurate diagnosis, and deliver effective care and therapy.1 These skills are the most important practices in the medical and clinical field that will help reach the best healthcare outcome and patient gratification.2 Furthermore, the physician-patient relationship has two main components: emotional and informational. Delivering successful medical care and intervention require strong physician-patient communication by establishing a partnership. Also, a patient has a great view about his or her medical situation or problem that will help him or her to be part of the decision making process.3 Therefore, creating a good social and personal relationship, facilitating sharing… ABBREVIATIONS: CDSS - Clinical Decision Support System, ICD - International Classification of Diseases, CPT - Current Procedural Terminology, SNOMED - Systematized Nomenclature of Medicine, HER - Electronic Health Records 1. Describe the patient-physician relationship. 2. Assess the current challenges of a healthy patient-physician relationship. 3. Evaluate the role of information technology in improving the relationship between patients and physicians. 4. Define the term clinical decision support system. 5. Discuss the benefits and challenges of clinical decision support systems. [1]: #corresp-1

The nowadays extensive use of computers in Medicine is remarkably widespread and covers almost all branches of the healthcare and biomedicine. Since the beginning of the computers rise, many scientists and doctors put their hopes in the aid that computers could provide in the medical decision-making based on their ability of storing and processing potential useful information/knowledge. Artificial Intelligence in Medicine has become a very important field in computer-aided medical research, covering various fields (e.g. automated diagnosis and therapy recommendation, image recognition and interpretation, patient management, tele-medicine/tele-health, etc.). A simple PubMed® search regarding the terms "Artificial Intelligence" and "Medicine" displays more than 2000 results, proving thus the huge interest in this field. The present paper presents a short review of some current Machine Learning algorithms (neural networks, genetic algorithms, support vector machines, Bayesian decision, k-nearest neighbor, etc.) used for automated diagnosis of different major diseases, such as breast, pancreatic and lung cancer, heart attacks, diabetes. Moreover, it also focuses on the intelligent patient management through Operations Research and Machine Learning techniques.

Can the cloud help the smallest, most vulnerable patients—those in the neonatal intensive care unit? Based upon the Artemis Cloud platform, a commercial service, researchers at McMaster Children’s Hospital built an analytical model to determine what would be needed to capture, analyze in real time, and store patient data. The Artemis cloud platform can collect up to 13 physiological measures (blood pressure, electrocardiogram, heart rate, etc.) plus infusion pump data from each patient, de-identify them, and transmit them to the cloud. There, medical algorithms—such as one that flags patients at risk for late onset neonatal sepsis and one that detects sleep apnea—are applied to streams of patient data. The data are stored for later retrospective analysis and data mining to uncover correlations and patterns of clinical relevance. Borrowing from queuing theory, the mathematical study of wait times originally used for calls routed through telephone exchanges, the researchers modeled the performance of Artemis Cloud. Patient features—gestational age, preterm/term, complex/standard—served as inputs to the model. Assumptions included: that service is rendered in order of patient arrival and that the unit operates at full capacity, 47 beds. The model predicted a mean of 47.8 patients in the entire intensive care unit, with a stay of 41.5 days per patient and 9 medical algorithms per patient per day. The researchers calculated that nearly 10 gigabytes of memory space would be needed to store both pump and patient data just for one day. A cluster of 40 central processing units could provide the required computational processing power for McMaster, even if the NICU grew from 47 to 170 beds. This study determines the infrastructure needed to support both computationally and memory-intensive activities. The idea of providing clinical support and early detection of disease through analytics is tantalizing, but only through practical implementation can we begin to reap the benefits of healthcare analytics. H. Khazaei et al ., Health informatics for neonatal intensive care units: An analytical modeling perspective. IEEE J. Transl. Eng. Health Med. 10.1109/JTEHM.2015.2485268 (2015). [[Full Text]][1] [1]: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7286745

This study describes an approach to building a clinical data bank on the basis of a standardized clinical process model. A clinical data bank is considered as a repository of clinical cases, which is medical knowledge reflecting both up-to-date and retrospective medical data. In view of the case-based nature of the decision making process in medicine, a clinical data bank can be used in clinical processes to make diagnosis and treatment decisions. The study discusses the following issues: a) choosing an approach to modeling a clinical process; b) completeness of the model; c) medical data standardization and normalization; d) plain medical text mining; d) big medical data analysis. The study also looks at metric-based methods of searching for relevant clinical cases in the data bank.

There was fulfilled systems analysis of decision making process for systemic medical research. The structure of the system was analysed. There were executed project research with help of UML-tools, allowing to fulfill project and software implementation of decision support system.

Indicators of medical quality is the main basis of medical quality evaluation, predictive analyzing the indicators by using data mining technology, we can find and solve the problems of hospital quality management more timely. The study, basing on the medical homepage records, analyzes the key indicators of the hospital medical quality by decision tree C4.5 algorithm. Decision tree is a prediction model which can assist hospital to make related decision on medical quality management issues. By using decision tree C4.5 algorithm with appropriate modification, a high quality model of decision tree can be made. It can not only do the statistic of medical quality indicators, but also predict the indicators with high relative accuracy to improve hospital's medical quality management.

Intelligent clinical decision support systems have been increasingly used in health care organisations. These systems are intended to help physicians in their diagnosis procedures; making decisions more accurate and effective, minimising medical errors, improving patient safety and reducing costs. However, the effectiveness and accuracy of these systems largely depend on the underlying AI technique that has been used, where same clinical-related problem can be solved using more than one AI technique which may provide different outcomes. Consequently, it is crucial to figure out the ideal utilisation of AI techniques in the clinical decision support systems. Our research study reviews various researches which utilised Artificial Intelligence techniques in clinical decision support systems with the aim of identifying basic criterion for adequate use of intelligent techniques within such systems. This paper presents a yes/no inquiry approach based on observations of previous research studies. The objective of this inquiry is to facilitate the selection of the most beneficial and effective AI technique that can be applied in the medical decision support system to provide the best outcomes.

The information's systems have become in indispensable elements in different technology fields and health area has been a great beneficiary. This type of system allow timely delivery and accurate of medical information which converts an indispensable input for attendant a timely manner a medical emergency. This work is focused in the design and development of intelligent system for classification in the care of patients of health system in Colombia. The paper describe aspects relatives with architectural system that can perform medical diagnostics to patients who have been classified low priority and would be left long periods of time to be served.

Introduction Clinical decision support systems (CDSSs) are being developed to assist physicians in processing extensive data and new knowledge based on recent scientific advances. Structured medical knowledge in the form of clinical alerts or reminder rules, decision trees or tables, clinical protocols or practice guidelines, score algorithms, and others, constitute the core of CDSSs. Several medical knowledge representation and guideline languages have been developed for the formal computerized definition of such knowledge. One of these languages is Arden Syntax for Medical Logic Systems, an International Health Level Seven (HL7) standard whose development started in 1989. Its latest version is 2.10, which was presented in 2014. In the present report we discuss Arden Syntax as a modern medical knowledge representation and processing language, and show that this language is not only well suited to define clinical alerts, reminders, and recommendations, but can also be used to implement and process computerized medical practice guidelines. Methods This section describes how contemporary software such as Java, server software, web-services, XML, is used to implement CDSSs based on Arden Syntax. Special emphasis is given to clinical decision support (CDS) that employs practice guidelines as its clinical knowledge base. Results Two guideline-based applications using Arden Syntax for medical knowledge representation and processing were developed. The first is a software platform for implementing practice guidelines from dermatology. This application employs fuzzy set theory and logic to represent linguistic and propositional uncertainty in medical data, knowledge, and conclusions. The second application implements a reminder system based on clinically published standard operating procedures in obstetrics to prevent deviations from state-of-the-art care. A to-do list with necessary actions specifically tailored to the gestational week/labor/delivery is generated. Discussion Today, with the latest versions of Arden Syntax and the application of contemporary software development methods, Arden Syntax has become a powerful and versatile medical knowledge representation and processing language, well suited to implement a large range of CDSSs, including clinical-practice-guideline-based CDSSs. Moreover, such CDS is provided and can be shared as a service by different medical institutions, redefining the sharing of medical knowledge. Arden Syntax is also highly flexible and provides developers the freedom to use up-to-date software design and programming patterns for external patient data access.

This paper concerns 4-valued logic as a basis for decision support system. We extend classic logic containing 2 values T, F with additional values C (contradiction) and U (uncertainty). This values are introduced for processing contradictory and incomplete information. We consider a generalized interpretation of arguments which determines predicate values for subsets. As a demonstration we consider the application of 4-valued logic for medical prescriptions.

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, are not machine-comprehensible and are not easily accessible to clinicians at the point of care. We have designed and implemented a web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in the chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation and a formal representation. These formats support increasingly sophisticated computational tasks. Guidelines can thus be in a hybrid representation in which guidelines, and even parts of the same guideline, might exist at different formalisation levels. We have also developed and rigorously evaluated a methodology and an associated web-based tool, Uruz, for gradually structuring and semi-formalising free-text clinical guidelines. Finally, we have designed, implemented and evaluated a new approach, the hybrid runtime application model, for supporting runtime application of clinical guidelines that are not necessarily in a machine-comprehensible format; in particular, when the guideline is in a semi-formal representation and the patient's data are either in an electronic medical record or in a paper format. The tool implementing this new approach, the Spock module, is customised at this point to the Asbru guideline specification language and exploits the hybrid structure of guidelines in DeGeL. The Spock module also exploits our temporal-abstraction mediator to the patient record, IDAN, and our interactive intelligent-visualisation tool, KNAVE-II.

Background. In the late 1990s, medical error came into focus as a problem to be explicitly acknowledged and addressed. Research on this topic is amassing in the epidemiology of medical error and the system and human factors that contribute to error. In addition, however, an understanding of medical errors in terms of the underlying decision process is needed. Objective. To present an individual-based framework for the study of medical errors in the context of the decision maker. Results. A framework is developed in terms of four state spaces: the decision environment, problem, goal, and action spaces. The role of information uncertainty is discussed. The framework is purposefully simple to provide flexibility and options for research-specific extensions, but sufficient structure is imposed to guide understanding and investigation. Conclusion. Understanding medical error in terms of the proposed framework can guide research and subsequent interventions by illuminating where in the decision process such errors are generated.

Causal probabilistic networks provide a natural framework for representation of medical knowledge, allowing clinical experts to encode assumptions about dependencies between stochastic variables. Application in medical decision support has produced promising results. However, model parameters may vary between patient groups or over time. Therefore methods are needed that allow for easy calibration of the model to a change in conditions. A solution to this problem is presented and illustrated with an example from a medical decision support system.

We medical software is becoming important means to secure competitive power of medical service by explosive increase of medical device based on software, and the quality of medical service is affected to quality of medical software as well as medical devices. But domestic related study and development is not sufficient for quality of medical software, and a falling-off in medical software quality can induce a falling-off in quality of medical service. Therefore, it is necessary to raise qualitative level of medical service by progress and quality improvement of medical devices. Therefore, in this paper, we developed test module and qualify inspection table which can test medical software and produce result based on ISO/1EC 12119.

The basic, accepted tenet of every physician and every obstetrician-gynecologist’s treatment philosophy is to provide the highest quality, cost effective medical care to each and every woman in their practice. Given this fact, our direction, response, and therapeutic goals for all patients at each and every branch of the medical algorithm decision tree should be no less than that expected or desired for one of our beloved family members. All aspects of our daily practice, including but not limited to decisions regarding appropriate spacing of appointment times, careful correct prescribing practices, thorough history taking, appropriate and complete physical examination, a resolve to relieve pain, thoughtful preoperative preparation, and successful safe completion of every surgical procedure should by governed by these “standards.” While every decision regarding women’s health care carries potential value and possible consequences, few carry as much psychologic or physiologic impact on short- or long-term patient well being as the correct diagnosis and appropriate management of a suspected gynecologic malignancy.

Formalized stages medical-diagnostic process at development computer decision support system in medicine. For complex estimation stage medical-diagnostic process for the reason minimization risk doctor-mistake is designed method to clusterizations diagnosis in space pharmacological action and correction threshold in diagnostic solving rule.

DocBot is a web-based clinical decision support system (CDSS) that uses patient interaction and electronic health record analytics to assist medical practitioners with decision making. It consists of two distinct HTML interfaces: a preclinical form wherein a patient inputs symptomatic and demographic information, and an interface wherein a medical practitioner views patient information and analysis. DocBot comprises an improved software architecture that uses patient information, electronic health records, and etiologically relevant binary decision questions (stored in a knowledgebase) to provide medical practitioners with information including, but not limited to medical assessments, treatment plans, and specialist referrals.

Soft Computing (SC) techniques are based on exploiting human knowledge and experience and they are extremely useful to model any complex decision making procedure. Thus, they have a key role in the development of Medical Decision Support Systems (MDSS). The soft computing methodology of Fuzzy Cognitive Maps has successfully been used to represent human reasoning and to infer conclusions and decisions in a human-like way and thus, FCM-MDSSs have been developed. Such systems are able to assist in critical decision-making, support diagnosis procedures and consult medical professionals. Here a new methodology is introduced to expand the utilization of FCM-MDSS for learning and educational purposes using a scenario-based learning (SBL) approach. This is particularly important in medical education since it allows future medical professionals to safely explore extensive "what-if" scenarios in case studies and prepare for dealing with critical adverse events.

He traditional human-aided medical guide service costs heavy human resources. The on-line medical guide service is almost human-aided and has uncertain waiting time for patients. Existing medical guidance systems still have insufficiency for improvement in reliability and correctness. This paper proposed an intelligent medical guide system based on multi-word TF-IDF algorithm, which has been implemented on Android mobile platform. This system applies the improved TF-IDF algorithm and the cosine similarity algorithm to calculate the possibility of disease for patients, and gives the user reasonable guidance. The related experiments proved that the improved TF-IDF algorithm in this paper increased the correctness and reliability of medical guidance.

Keeping current given the vast volume of medical literature published yearly poses a serious challenge for medical professionals. Thus, interest in systems that aid physicians in making clinical decisions is intensifying. A task of Clinical Decision Support (CDS) systems is retrieving highly relevant medical literature that could help healthcare professionals in formulating diagnoses or determining treatments. This search task is atypical as the queries are medical case reports, which differs in terms of size and structure from queries in other, more common search tasks. We apply query reformulation techniques to address literature search based on case reports. The proposed system achieves a statistically significant improvement over the baseline (29% – 32%) and the state-of-the-art (12% – 59%).

This paper proposes a new method of patterns analysis using SOM in medical data sets for medical treatment service under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to classify disease patterns in the medical historical record to join the information of patient, using SOM neural network with input vectors of different features, disease code, input factors in order to take the medical treatment service in medical data sets, to reduce patients' search effort to get the information of diagnosis for recovering their health and to improve the rate of accuracy. To verify improved performance, we make experiments with dataset collected in medical center.

This work aims to find the medical information suited to the conditions, by analyzing the keywords provided from an user. With the given information, this model can be used to provide a flexible, compatible and effective searching paper for medical information. The proposed framework which helps medical institutions to search medical information in conjunction with analysis of user's request. The framework mainly includes key component of metadata for searching the medical information suited to the conditions.

Understanding the human body dynamics in response to any medical treatment makes automated decision support systems for healthcare quite complex. In this paper, we present an architecture for Medical Cyber-Physical Systems to help developers to generate test cases for their applications using models already validated. It is based on component models to simulate the operation of medical devices and patient data. Medical guidelines and a clinical database have been used together with statistical techniques to create regression models that simulate vital signs. A controlled experiment of a clinical scenario has been developed to validate the proposed architecture components. The results of this study indicate that models for the healthcare domain are a promising alternative to test their applications.

A new method for “cooperative decision making” between physicians and patients during medical decision process is created. MAU (Multi-Attribute Utility) Model and Fuzzy logic for medical decision making is combined. Patients and physicians can negotiate with each other and come out with acceptable solution for medical treatment decision. Both of patient and physician would trade-off their values to reach consensus in choosing medical treatment alternatives. This method can also be generalized to those decision processes involving an expert and a novice. The combined decision model would improve the quality of cooperative decision making.

The stochastic tree is a recently introduced generalization of the decision tree which allows the explicit depiction of temporal uncertainty while still employing the familiar rollback procedure for decision trees. We offer an introduction to stochastic-tree modeling and techniques involved in their application to medical-treatment decisions. We also describe an application of these tools to the analysis of the decision to undergo a total hip replacement from the perspectives of an individual patient (via utility analysis) and of society (via cost-effectiveness analysis).

This paper discusses the application of artificial intelligence in telemedicine and some of our research results in this area. The main goal of our research is to develop methods and systems to collect, analyse, distribute and use medical diagnostics knowledge from multiple knowledge sources and areas of expertise. Use of modern communication tools enable a physician to collect and analyse information obtained from experts worldwide with the help of a decision support medical system. In this paper we discuss a multilevel representation and processing of medical data using a system which evaluates and exploits knowledge about the behaviour of statistical diagnostics methods. The presented technique is able to acquire semantically-essential information from the complex dynamics of quasi-periodical medical signals by applying recursively-ordinary statistical tools. A method and an algorithm are elaborated to select automatically the most appropriate diagnostics method for each case under consideration. We suggest the use of a voting-type technique to search for consensus among the different opinions of medical experts. Research results can be applied in the development of a telediagnostics expert medical system and medical teleconsulting support system.

The Patient Interview Support Application (PISA) combines natural-language processing with a knowledge base to let clerks conduct medical intake interviews, rather than physicians or triage nurses. More complete medical records result, leading to improved physician-patient interaction and more cost-effective care.

Medical databases serve as rich knowledge sources for effective medical diagnosis. Recent advances in medical technology and extensive usage of electronic medical record systems, helps in massive production of medical text data in hospitals and other health institutions. Most of this text data that contain valuable information are just filed and not utilized to the full extent. Proper usage of medical information can bring about tremendous changes in medical field. This paper present a new method of uncovering valid association rules from medical transcripts. The extracted rules describes association of disease with other diseases, symptoms of a particular disease, medications used for treating diseases, the most prominent age group of patients for developing a particular disease. NLP (Natural Language Processing) tools were combined with data mining algorithms (Apriori algorithm and FP-Growth algorithm) for the extraction of rules. Interesting rules were selected using the correlation measure, lift.

Artificial Intelligence techniques have been increasingly used in medical decision support systems to aid physicians in their diagnosis procedures; making decisions more accurate and effective, minimizing medical errors, improving patient safety and reducing costs. Our research study indicates that it is difficult to compare different artificial intelligence techniques which are utilised to solve various medical decision-making problems using different data models. This makes it difficult to find out the most useful artificial intelligence technique among them. This paper proposes a classification approach that would facilitate the selection of an appropriate artificial intelligence technique to solve a particular medical decision making problem. This classification is based on observations of previous research studies.

Technological advances have broadened the capabilities and application of computers, so producing new smart knowledge-based systems aimed at emulating cognitive processes underlying medical decision making for improving patient care and health outcomes. In such a context, this paper proposes a novel approach to emulate cognitive processes in medical decision making, based on a fuzzy extension of standard semantic technologies, namely ontologies and rule engines. It consists into a formal model combining ontological primitives for reproducing information entities of the domain of interest and fuzzy linguistic statements for expressing uncertainty as relative grades of information. On the top of this model, cognitive processes underlying medical decision making are emulated by defining chains of hybrid logic rules involving both precise and vague information. An application case is provided to highlight how the approach can be used to encode medical decision making in managing patients affected by Chronic Obstructive Pulmonary Disease.

Computer Interpretable Guidelines (CIG) are an emerging area of research, to support medical decision making through evidence-based recommendations. However, new challenges in the data management field have to be faced, to integrate CIG management with a proper treatment of patient data, and of other forms of medical knowledge. The GINSENG (GuIdeliNe SEcoNd Generation) project aims at setting the ground for the proposal of a four-year European project (in the HORIZON 2020 context) coping with such issues. The "visionary" idea underlying our long-term research proposal is that, to achieve a significant step forward in the state-of-the-art, it is important to provide an homogeneous approach integrating (at least) three different aspects (which have been often considered in isolation by the Medical Informatics literature): (1) Computer Interpretable Guidelines, (2) Treatment of patient data, and (3) Treatment of "basic" medical knowledge (BMK).

A well-controlled, meticulous process has a far higher probability of resulting in a high quality of medical care than improvisation and unstructured creativity. Algorithms display decision-making treatment processes and problem-solving strategies by giving clearly defined and formalized guidelines. The flow chart for decision-making follows the yes/no dichotomy of binary logic. The systematic ordering of decision points and consequent actions is guided by medical priority and thus regulates the time-frame and sequence of each single step in a logical manner. With the help of clinical algorithms highly complex processes such as the management of the severely injured patient can be translated into a clearly structured, logical pathway. Clinical algorithms represent scientifically recognized treatment rules, indicate a solution for solving problems and help users to organize ideas and recognize connections. They delineate a consistent and valid guideline, while allowing deviations in proven exceptions. The use of algorithms allows a systematic search for errors in the process of quality management. In emergency situations they suggest a structured means of problem solving for the less experienced user. Algorithms are useful instruments in the teaching of medical decision-making.

The Arden Syntax for Medical Logic Modules standard language for knowledge-based computer systems is described. Knowledge systems can use computerized patient data in decision-making. Although they have the potential to reduce adverse drug events and infection rates, improve drug dosing, and decrease the cost of care, knowledge systems have not yet reached the average patient. Arden Syntax for Medical Logic Modules is the standard language for defining clinical decision rules that drive alerts, reminders, clinical guidelines, and data interpretations. Each medical logic module (MLM) in an Arden Syntax knowledge base is designed to make one type of decision. MLMs usually represent either rules that can be encoded, such as generating a warning that the potassium concentration is decreasing in a patient taking digoxin, or complex decision trees for individual patient care plans and clinical protocols. An MLM contains maintenance slots (title, file name, version, originating institution, author, date, specialist, validation information), library slots (stating the MLM's purpose and providing keywords for searching), and knowledge slots (containing the "essence" of the MLM). Arden Syntax is receiving growing support from the medical and information systems communities as the standard language for medical knowledge systems, but legal, ethical, regulatory, and ownership issues remain. If pharmacy is to grow and prosper as a knowledge profession, it should adopt an accepted standard language for representing active, applied knowledge in computer systems.

Introduction: Prevalence of new diseases, medical science promotion and increase of referring to health care centers, provide a good situation for medical errors growth. Errors can involve medicines, surgery, diagnosis, equipment, or lab reports. Medical errors can occur anywhere in the health care system: In hospitals, clinics, surgery centers, doctors’ offices, nursing homes, pharmacies, and patients’ homes. According to the Institute of Medicine (IOM), 98,000 people die every year from preventable medical errors. In 2010 from all referred medical error records to Iran Legal Medicine Organization, 46/5% physician and medical team members were known as delinquent. One of new technologies that can reduce medical errors is clinical decision support systems (CDSSs). Methods: This study was unsystematic-review study. The literature was searched on evaluate the “ability of clinical decision support systems to improve clinical practice” with the help of library, books, conference proceedings, data bank, and also searches engines available at Google, Google scholar. For our searches, we employed the following keywords and their combinations: medical error, clinical decision support systems, Computer-Based Clinical Decision Support Systems, information technology, information system, health care quality, computer systems in the searching areas of title, keywords, abstract, and full text. In this study, more than 100 articles and reports were collected and 38 of them were selected based on their relevancy. Discussion and conclusion: The CDSSs are computer programs, designed for help to health care careers. These systems as a knowledge-based tool could help health care manager in analyze evaluation, improvement and selection of effective solutions in clinical decisions. Therefore, it has a main role in medical errors reduction. The aim of this study was to express ability of the CDSSs to improve clinical practice.

Artificial intelligence is the capability of computing machines to perform at par with humans in some cognitive tasks. Since its conception in the 1940s, AI has ambitiously evolved to naturally and comfortably immerse in extraordinary and multidisciplinary fields including computer science, education, engineering and medicine. This survey aims to provide and highlight the importance of AI work in the field of medical informatics and biomedicine. We have reviewed latest AI research in this immense field of medical science with special attention given to medical diagnosis. Various intelligent computing tools from rule-based expert systems and fuzzy logic to neural networks and genetic algorithms used in medical diagnosis were considered. We have explored hydrocephalus, a medical condition causing headaches. We also analysed a prototype of what is known as NeuroDiary web application that is currently being tested as a software mobile application for collecting data of patients with hydrocephalus. We finally propose the development of an expert mobile application system to assist clinicians in the diagnosis, analysis and treatment of hydrocephalus.

The overwhelmed amount of medical information available online, makes the use of automated recommendation methods essential for identifying relevant information according to user profile needs. This paper presents a method to address the problem of medical document classification into documents for medical professionals (experts) and non-professionals (consumers). Documents are represented by terms extracted from AMTEx, a medical document indexing method, specifically designed for the automatic indexing of documents in large medical collections, such as MEDLINE, and then mapped to the UMLS Semantic Network (SN) categories. Multiple Criteria Decision Analysis (MCDA) tools are applied to calculate the membership of each SN category to the document classification. Several factors such as the classification nature of the problem and the incorporation of common readability formulas are also examined.

During years, the development of medical systems (as part of Clinical Decision Support Systems (CDSS)) has been one of the main research fields in biomedical informatics area. Different systems have been proposed to improve the quality of medical practice in several different areas such as pharmacy management, electronic health records, diagnosis systems, telemedicine, and medical imaging among others. Several approaches and models have been developed since, the earliest 60s. Also several methods and techniques from the fields of artificial intelligence, decision theory, and statistics have been introduced into models of the medical management of patients (diagnosis, treatment, and follow-up); in some of these models, assessment of the expected prognosis constitutes an integral part. Typically, recent prognostic methods rely on explicit (patho)physiological models, which may be combined with traditional models of life expectancy. The main goals of this special issue were the publication of new algorithms and mathematical methods, decision theories, techniques, and models based on probabilistic and quantitative approaches to solve existing or new issues.

In this chapter a software system for the automated detection of melanoma over dermoscopic images is presented. The analysis is carried out by supporting in the “ABCD Rule” medical algorithm, undertaking the automated detection and characterization of the corresponding indicators. For this purpose the system uses different image processing techniques and three supervised machine learning tasks. To test the robustness of the system the different indicators of the algorithm are tested, obtaining good results of accuracy in all of them, and there also was determined in a direct way the diagnostic capacity of the system, obtaining results of 81.25 % of sensitivity and 77.14 % of specificity. Moreover, the system is also capable of analyzing macroscopic images, having been designd with multiplatform architecture and being firmly oriented to teledermatology, which is increasingly used.

The human factor is the cause, interacting party and object of intelligent systems in medicine. This paper deals with the failures of successful interaction between medical hardware, software and personnel which ultimately leads to medical errors; their correlation to medical legal consequences, costs, usability and safety discussing possible amendments. The present work besides presenting a multilevel and interdisciplinary theoretical and practical approach, researched practical cases having direct influence on morbidity and mortality.

Medical diagnostic decision-making is a complex task that consists in finding the right diagnosis from the signs and symptoms presented by a patient. Computers have rapidly been considered as potential diagnostic aids in medical decision-making. This chapter first presents medical diagnostic modeling as a hypothetico-deductive reasoning process. Then, the different approaches developed to provide computerized medical diagnostic decision support are proposed. Initial numerical approaches, either statistical or probabilistic, are first presented. Examples of clinical scores, more recently developed, are given. Then, medical expert systems are described. The three components of an expert system, the knowledge base, the base of facts, and the inference engine, are introduced. A focus is given on knowledge representation formalisms with the description of production rules, decision trees, semantic networks, and frames. The subsection describing the inference engine starts with a presentation of the three types of inference (deduction, induction, abduction). The principles of formal logic are given and the main ways the inference engine may operate are described (forward and backward chainings). Finally, historical medical expert systems, such as Mycin and Internist, as well as systems currently available for medical diagnostic decision support (DXplain™) are described.