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Automated probabilistic transformation of a large medical diagnostic support system.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated probabilistic transformation of a large medical diagnostic support system./
作者:	Li, Yu-Chuan,.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 1995,
面頁冊數:	175 p.
附註:	Source: Dissertations Abstracts International, Volume: 56-10, Section: A.
Contained By:	Dissertations Abstracts International56-10A.
標題:	Information Systems. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9514454
ISBN:	9798208909782

Automated probabilistic transformation of a large medical diagnostic support system.
Li, Yu-Chuan,.

Automated probabilistic transformation of a large medical diagnostic support system. - Ann Arbor : ProQuest Dissertations & Theses, 1995 - 175 p.

Source: Dissertations Abstracts International, Volume: 56-10, Section: A.

Thesis (Ph.D.)--The University of Utah, 1995.

This item must not be sold to any third party vendors.

Iliad is a medical diagnostic decision support system with a very large knowledge base (KB) focused on internal medicine diseases. It uses a special knowledge representation (KR) (the Iliad-KR) for flexible and efficient encoding of medical knowledge. Due to the heuristic nature of the Iliad-KR, probabilities generated by the system have been found to be less than sound. In this dissertation, I proposed a probabilistic KR named Bayesian networks as an alternative to the Iliad-KR and describe a set of algorithms that can transform any KB in Iliad-KR form into a Bayesian network automatically. A two-part experiment was conducted to evaluate the performance of the Iliad-KR and the Bayesian network alternative. The first part was a feasibility. Here I transformed a small KB into Bayesian network form and used a set of statistical performance indices to evaluate the probabilities generated by the Iliad-KR and the Bayesian network model respectively. This study demonstrated the feasibility of such transformation and also suggested that the Bayesian network model is more reliable and discriminative than the Iliad-KR model. The second part of the experiment was a behavioral study. Here the complete Iliad KB for internal medicine was transformed into a large Bayesian network. Twenty patients from two domains of internal medicine were evaluated by four subspecialists from these domains (two in each domain). Diagnostic suggestions generated by the Iliad-KR and the Bayesian network models were given to these physicians and the impact of these suggestions on the physicians' diagnostic decision was measured. The results suggested that computerized diagnostic systems can affect the behavior of physicians and that the Bayesian network model had more positive influence on physicians' diagnosis than the Iliad-KR model.

ISBN: 9798208909782Subjects--Topical Terms:

769307
Information Systems.
Subjects--Index Terms:

Bayesian networks

Automated probabilistic transformation of a large medical diagnostic support system.
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300 $a 175 p.
500 $a Source: Dissertations Abstracts International, Volume: 56-10, Section: A.
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506 $a This item must not be sold to any third party vendors.
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520 $a Iliad is a medical diagnostic decision support system with a very large knowledge base (KB) focused on internal medicine diseases. It uses a special knowledge representation (KR) (the Iliad-KR) for flexible and efficient encoding of medical knowledge. Due to the heuristic nature of the Iliad-KR, probabilities generated by the system have been found to be less than sound. In this dissertation, I proposed a probabilistic KR named Bayesian networks as an alternative to the Iliad-KR and describe a set of algorithms that can transform any KB in Iliad-KR form into a Bayesian network automatically. A two-part experiment was conducted to evaluate the performance of the Iliad-KR and the Bayesian network alternative. The first part was a feasibility. Here I transformed a small KB into Bayesian network form and used a set of statistical performance indices to evaluate the probabilities generated by the Iliad-KR and the Bayesian network model respectively. This study demonstrated the feasibility of such transformation and also suggested that the Bayesian network model is more reliable and discriminative than the Iliad-KR model. The second part of the experiment was a behavioral study. Here the complete Iliad KB for internal medicine was transformed into a large Bayesian network. Twenty patients from two domains of internal medicine were evaluated by four subspecialists from these domains (two in each domain). Diagnostic suggestions generated by the Iliad-KR and the Bayesian network models were given to these physicians and the impact of these suggestions on the physicians' diagnostic decision was measured. The results suggested that computerized diagnostic systems can affect the behavior of physicians and that the Bayesian network model had more positive influence on physicians' diagnosis than the Iliad-KR model.
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