Journal article
Dynamic network construction and updating techniques for the diagnosis of acute abdominal pain
IEEE transactions on pattern analysis and machine intelligence, v 15(3)
01 Jan 1993
Abstract
Computing diagnoses in domains with continuously changing data is a difficult but essential aspect of solving many problems. To address this task, this paper describes a dynamic influence diagram (ID) construction and updating system (DYNASTY) and its application to constructing a decision-theoretic model to diagnose acute abdominal pain, which is a domain in which the findings evolve during the diagnostic process. For a system that evolves over time, DYNASTY constructs a parsimonious ID and then dynamically updates the ID, rather than constructing a new network from scratch for every time interval. In addition, DYNASTY contains algorithms that test the sensitivity of the constructed network's system parameters. The main contributions of this paper are 1) presenting an efficient temporal influence diagram technique based on parsimonious model construction and 2) formalizing the principles underlying a diagnostic tool for acute abdominal pain that explicitly models time-varying findings.
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Details
- Title
- Dynamic network construction and updating techniques for the diagnosis of acute abdominal pain
- Creators
- Gregory Provan - Dept. of Comput. & Information Sci., Pennsylvania Univ., Philadelphia, PA, USAJohn Clarke - [Department of Surgery, Medical College, Philadelphia, PA, USA]
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, v 15(3)
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:A1993KT65800012
- Scopus ID
- 2-s2.0-0027558641
- Other Identifier
- 991019183917804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic