An appraisal of INTERNIST-I

Wolfram, D. A.

doi:10.1016/0933-3657(94)00028-q

Cited by 36 publications

(13 citation statements)

References 48 publications

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“…Because of the vastness of the knowledgebase and the amount of manpower and commitment required to develop and maintain it, a sufficient database has been very difficult to achieve using traditional development methods [22]. For example, despite expending nearly 25-30 person years of work, it has still not been able to complete the knowledgebase of INTERNIST-I, an expert diagnostic system in Internal Medicine [1].…”

Section: Discussionmentioning

confidence: 99%

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ST Algorithm for Medical Diagnostic Reasoning

Fernando¹,

Henskens²

2013

Polibits

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The authors have previously described an approach for medical diagnostic reasoning based on the ST (Select and Test) model introduced by Ramoni and Stefanelli et al. This paper extends the previous approach by introducing the required algorithm for medical expert system development. The algorithm involves a bottom-up and recursive process using logical inferences, abduction, deduction, and induction. Pseudocode for the algorithm, and the data structures involved, are described, and the algorithm's implementation using a small sample knowledgebase and programmed in Java is included in appendixes. Implementation of a successful expert system is a challenging process; development of the necessary algorithm for its inference engine, and definition of a knowledgebase structure that models expert diagnostic reasoning and knowledge, only fulfils the initial step. Challenges associated with the remaining steps of the development process can be identified and dealt with using the CLAP software process model.

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Section: Discussionmentioning

confidence: 99%

“…ealisation of medical expert systems has been one of the earliest goals of the AI community. Unfortunately, attempts by major projects such as INTERNIST -I and CADUCEUS have not been successful [1].…”

Section: Introductionmentioning

confidence: 99%

ST Algorithm for Medical Diagnostic Reasoning

Fernando¹,

Henskens²

2013

Polibits

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“…Even though the realisation of medical expert systems was one of the earliest goals of the AI community, some of the largest projects undertaken, for example, INTERNIST-I and CADUCEUS, have been unsuccessful [5].…”

Section: Related Approachesmentioning

confidence: 99%

Drill-Locate-Drill Algorithm for Diagnostic Reasoning in Psychiatry

Fernando¹,

Henskens²

2013

IJMLC

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Abstract-This paper introduces a top-down algorithm for diagnosing psychiatric illnesses. It is based on the conceptualisation of diagnostic categories, diagnosis, and symptoms as a hierarchical model. The algorithm assumes that there exist a few close-ended clinical questions that can be used during clinical interview to rule in and rule out diagnostic categories, diagnoses and their symptoms. Compared to a more exhaustive bottom-up and recursive algorithm, which the authors have previously introduced, this algorithm has the advantage of being easy to implement requiring a less extensive knowledgebase. It is expected the algorithm will be used as a useful screening tool that increases the detection of psychiatric disorders, which are common but unfortunately currently under-diagnosed.

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“…There are many other terms for WBE, such as online education, virtual education, internet-based education, etc. (25, 28, 30)…”

Section: Web Based Learningmentioning

confidence: 99%

Information Technologies (ITs) in Medical Education

Mašić¹,

Pandža²,

Toromanović³

et al. 2011

Acta Inform Med

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Advances in medicine in recent decades are in significant correlation with the advances in the information technology. Modern information technologies (IT) have enabled faster, more reliable and comprehensive data collection. These technologies have started to create a large number of irrelevant information, which represents a limiting factor and a real growing gap, between the medical knowledge on one hand, and the ability of doctors to follow its growth on the other. Furthermore, in our environment, the term technology is generally reserved for its technical component. Education means, learning, teaching, or the process of acquiring skills or behavior modification through various exercises. Traditionally, medical education meant the oral, practical and more passive transferring of knowledge and skills from the educators to students and health professionals. For the clinical disciplines, of special importance are the principles, such as, “learning at bedside,” aided by the medical literature. In doing so, these techniques enable students to contact with their teachers, and to refer to the appropriate literature. The disadvantage of these educational methods is in the fact, that teachers often do not have enough time. Additionally they are not very convenient to the horizontal and vertical integration of teaching, create weak or almost no self education, as well as, low skill levels and poor integration of education with a real social environment. In this paper authors describe application of modern IT in medical education – their advantages and disadvantages comparing with traditional ways of education.

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An appraisal of INTERNIST-I

Cited by 36 publications

References 48 publications

ST Algorithm for Medical Diagnostic Reasoning

ST Algorithm for Medical Diagnostic Reasoning

Drill-Locate-Drill Algorithm for Diagnostic Reasoning in Psychiatry

Information Technologies (ITs) in Medical Education

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