An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

A Design Science Research Methodology for Expert Systems Development

The knowledge of design science research (DSR) can have applications for improving expert systems (ES) development research. Although significant progress of utilising DSR has been observed in particular information systems design – such as decision support systems (DSS) studies – only rare attempts can be found in the ES design literature. Therefore, the aim of this study is to investigate the use of DSR for ES design. First, we explore the ES development literature to reveal the presence of DSR as a research methodology. For this, we select relevant literature criteria and apply a qualitative content analysis in order to generate themes inductively to match the DSR components. Second, utilising the findings of the comparison, we determine a new DSR approach for designing a specific ES that is guided by another result – the findings of a content analysis of examination scripts in Mathematics. The specific ES artefact for a case demonstration is designed for addressing the requirement of a ‘wicked’ problem in that the key purpose is to assist human assessors when evaluating multi-step question (MSQ) solutions. It is anticipated that the proposed design knowledge, in terms of both problem class and functions of ES artefacts, will help ES designers and researchers to address similar issues for designing information system solutions

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

تم عمل نظام دعم القرار السريري الآلي (CDSS) كنموذج جديد في الخدمات الطبية. بحيث يتم استخدام CDSSs لمساعدة الأخصائيين (الأطباء) في اتخاذ قراراتهم المحيرة. ولهذا السبب ، تم بناء DSS اعتمادًا على معرفة الأطباء وباستخدام استخراج البيانات لمساعدة خلية الازمة الطبية للسيطرة على جائحة فيروس COVID-19 ، وبشكل عام ، لتحديد الفئة من العدوى وتقديم علاج بروتوكول مناسب حسب أعراض المريض. في البداية لتشخيص المرض تم الاعتماد على ثلاث اعراض اولية هي ( الحمى, التعب والسعال الجاف) لمعرفة الشخص المصاب وعند تحديد أي من هذه الاعراض يتم تقسيم الاشخاص المصابين الى اربعة اصناف حسب مناعة الاشخاص ( اصابة طفيفة , اصابة عالية , اصابة شديدة جدا و طبيعي). وايضا يتم التشخيص باستخدام عاملين هما ( عمر المريض و الامراض المزمنة للمريض مثل السكر ومشاكل القلب وضغط الدم ) ثم يتم تقدير حالة المصاب حيث توجد ستة مستويات للاشخاص المصابين بفيروس كورونا 2019 وتحتاج الى عناية حسب حالة المصاب. عندما يكون الفحص موجب واعتمادا على عمر المريض والامراض المزمنة يتم تحديد في أي مستوى من المستويات الستة يكون المريض حسب الاعراض . وبذلك يتم تحديد درجة حالة المريض من الدرجات الاربع ثم يتم اقتراح اربعة بروتوكولات للعلاج ويتم اختيار الانسب حسب اختيار الاطباء وايضا يوفر النظام معلومات كاملة عن الوقاية وتجنب الوباء واخيرا يتم ارسال ايميل يحتوي جميع المعلومات من مركز السيطرة لللاشخاص المسؤولين . تم اعتماد خوارزمية C4.5  في شجرة اتخاذ القرار لبناء هذا التطبيق.                                                                                                                                              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree

Development of decision support system for the diagnosis of arthritis pain for rheumatic fever patients: Based on the fuzzy approach

Developing a Decision Support System (DSS) for Rheumatic Fever (RF) is complex due to the levels of vagueness, complexity and uncertainty management involved, especially when the same arthritis symptoms can indicate multiple diseases. It is this inability to describe observed symptoms precisely that necessitates our approach to developing a Decision Support System (DSS) for diagnosing arthritis pain for RF patients using fuzzy logic. In this paper we describe how fuzzy logic could be applied to the development of a DSS application that could be used for diagnosing arthritis pain (arthritis pain for rheumatic fever patients only) in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our approach employs a knowledge-base that was built using WHO guidelines for diagnosing RF, specialist guidelines from Nepal and a Matlab fuzzy tool box as components to the system development. Mixed membership functions (Triangular and Trapezoidal) are applied for fuzzification and Mamdani-type is used for the fuzzy reasoning process. Input and output parameters are defined based on the fuzzy set rules

The application of biomedical engineering techniques to the diagnosis and management of tropical diseases: A review

This paper reviews a number of biomedical engineering approaches to help aid in the detection and treatment of tropical diseases such as dengue, malaria, cholera, schistosomiasis, lymphatic filariasis, ebola, leprosy, leishmaniasis, and American trypanosomiasis (Chagas). Many different forms of non-invasive approaches such as ultrasound, echocardiography and electrocardiography, bioelectrical impedance, optical detection, simplified and rapid serological tests such as lab-on-chip and micro-/nano-fluidic platforms and medical support systems such as artificial intelligence clinical support systems are discussed. The paper also reviewed the novel clinical diagnosis and management systems using artificial intelligence and bioelectrical impedance techniques for dengue clinical applications