The treatment of time in a case-based analysis of experimental medical studies

Case-based prediction in experimental medical studies

Artificial Intelligence in Medicine ◽

10.1016/s0933-3657(98)00057-8 ◽

1999 ◽

Vol 15 (3) ◽

pp. 255-273 ◽

Cited By ~ 4

Author(s):

A. Seitz ◽

A.M. Uhrmacher ◽

D. Damm

Keyword(s):

Medical Studies ◽

Case Based

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Medical Case Based Reasoning Frameworks

International Journal of Decision Support System Technology ◽

10.4018/ijdsst.2016070103 ◽

2016 ◽

Vol 8 (3) ◽

pp. 31-62 ◽

Cited By ~ 4

Author(s):

Shaker El-Sappagh ◽

Mohammed Mahfouz Elmogy

Keyword(s):

English Language ◽

Diabetes Management ◽

Critical Evaluation ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Case Based Reasoning ◽

Medical Studies ◽

Medical Systems ◽

Medical Domain ◽

Case Based

Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

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Two Case-Based Systems for Explaining Exceptions in Medicine

Data Mining and Medical Knowledge Management ◽

10.4018/978-1-60566-218-3.ch011 ◽

2011 ◽

pp. 227-249

Author(s):

Rainer Schmidt

Keyword(s):

Medical Practice ◽

Knowledge Based Systems ◽

Medical Studies ◽

Knowledge Source ◽

Knowledge Based System ◽

Therapy Recommendations ◽

Knowledge Based ◽

Theoretical Hypothesis ◽

Two Systems ◽

Case Based

In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions shall be explained. In this chapter, we present two systems that deal with both sorts of these situations. The first one, called ISOR-1, is a knowledge-based system for therapy support. It does not just compute therapy recommendations, but it especially investigates therapy inefficacy. The second system, ISOR-2, is designed for medical studies or research. It helps to explain cases that contradict a theoretical hypothesis. Both systems are working in close co-operation with the user, who is not just considered as knowledge provider to build the system but is incorporated as additional knowledge source at runtime. Within a dialogue between the doctor and the system solutions respectively explanations are searched.

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Medical Case Based Reasoning Frameworks

Virtual and Mobile Healthcare ◽

10.4018/978-1-5225-9863-3.ch025 ◽

2020 ◽

pp. 516-552

Author(s):

Shaker El-Sappagh ◽

Mohammed Mahfouz Elmogy

Keyword(s):

Diabetes Management ◽

Critical Evaluation ◽

Case Based Reasoning ◽

Medical Studies ◽

Medical Systems ◽

Language Studies ◽

The Status ◽

Medical Domain ◽

Vague Data ◽

Case Based

Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

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Impact of covid-19 pandemic: Perception of clinical year medical students on transition of traditional to online lectures in a medical university of Karachi, Pakistan.

The Professional Medical Journal ◽

10.29309/tpmj/2021.28.05.5970 ◽

2021 ◽

Vol 28 (05) ◽

pp. 625-629

Author(s):

Shazia Fakhir ◽

Ammara Hameed

Keyword(s):

Clinical Teaching ◽

Faculty Training ◽

Medical Studies ◽

Online Student ◽

Online Lectures ◽

Medical University ◽

Component Training ◽

Study Setting ◽

Interaction Component ◽

Case Based

Objective: To determine the perception of clinical undergraduate MBBS students of online lectures in a medical university of Karachi, Pakistan. Study Design: Descriptive Study. Setting: Bahria University Medical and Dental College. Period: May 2020 till July 2020. Material & Methods: Four weeks after introduction of online lectures. Data was collected from clinical year students over two weeks using Google forms and analysed using SPSS version 22. Result: Of 450 clinical year students, n=234 responded. Overall, 48.7% (n = 114) students were satisfied with the online lectures, 34.2 %( n= 80) were completely satisfied and 17.7 %( n=39) were unsatisfied. 53.4 %( n=125) felt lectures were serving the purpose whereas 37.2% (n=87) do not feel the same. The lack of interaction component in online lectures was felt by 45.7% (n=107) students. Regarding clinical teaching, 72.2% (n= 170) do not think it is possible online, 15.8 (n=35) feel it possible and 12.4% (n=29) were hopeful. Majority 61.1% (n=143) think it is impossible to complete medical studies online. Student ideas for improvement included availability of lecture recording for later viewing, integrated quizzes, increasing interactive component, training of faculty, small group sessions and case based teaching. Majorly students faced internet connectivity issues and timings of long lectures without break. Conclusion: Online lectures can be improved by reducing the issues faced by students, providing them easy internet access, faculty training programs to make interactive and case based presentation and quizzes.

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