Experience with a model of sequential diagnosis

Gorry, G. Anthony; Barnett, G. Octo

doi:10.1016/0010-4809(68)90016-5

Cited by 162 publications

(45 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The earliest work on medical expert systems was based on Bayesian approach [10, 11]. Pioneer medical systems based on Bayesian networks included the Nestor system [12] for diagnosis of endocrinology disorders, Munin [13], a medical system for diagnosing neuromuscular disorders, and Alarm [14], a system monitoring patients in intensive care units.…”

Section: Bayesian Network Models In Medical Diagnosismentioning

confidence: 99%

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

Oniśko

Drużdżel

2013

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Objective One of the hardest technical tasks in employing Bayesian network models in practice is obtaining their numerical parameters. In the light of this difficulty, a pressing question, one that has immediate implications on the knowledge engineering effort, is whether precision of these parameters is important. In this paper, we address experimentally the question whether medical diagnostic systems based on Bayesian networks are sensitive to precision of their parameters. Methods and Materials The test networks include Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders and six other medical diagnostic networks constructed from medical data sets available through the Irvine Machine Learning Repository. Assuming that the original model parameters are perfectly accurate, we lower systematically their precision by rounding them to progressively courser scales and check the impact of this rounding on the models' accuracy. Results Our main result, consistent across all tested networks, is that imprecision in numerical parameters has minimal impact on the diagnostic accuracy of models, as long as we avoid zeroes among parameters. Conclusion The experiments' results provide evidence that as long as we avoid zeroes among model parameters, diagnostic accuracy of Bayesian network models does not suffer from decreased precision of their parameters.

show abstract

Section: Bayesian Network Models In Medical Diagnosismentioning

confidence: 99%

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

Oniśko

Drużdżel

2013

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

show abstract

“…Early leaders in computer-aided diagnosis developed statistical methods7 8 and models9 10 to serve as underpinnings for diagnostic systems. Shortliffe and colleagues skillfully organised these approaches into categories including: clinical algorithms, databank analysis, mathematical modelling of physical processes, statistical pattern recognition, Bayesian techniques, decision theory approaches and symbolic reasoning 11.…”

Section: Introductionmentioning

confidence: 99%

Use of health information technology to reduce diagnostic errors

2013

View full text Add to dashboard Cite

BackgroundHealth information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions.Methods A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified.ResultsHIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact.ConclusionsDiagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.

show abstract

“…See Section 4.3 for experimental verification. This is also the myopia approach (Gorry and Barnett, 1968). On the other hand, if the delay cost of the first attribute in a batch is very large, the current batch would grow until the expected cost reduction of the remaining unknown attributes is no longer greater than 0.…”

Section: Algorithmmentioning

confidence: 96%

Feature value acquisition in testing

Sheng

Ling

2006

Proceedings of the 23rd International Conference on Machine Learning - ICML '06

View full text Add to dashboard Cite

In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make an accurate diagnosis of patient diseases. While doing so they have to make a trade-off between the cost of the tests and possible misdiagnosis. In this paper, we use cost-sensitive learning to model this process. We assume that test examples (new patients) may contain missing values, and their actual values can be acquired at cost (similar to doing medical tests) in order to reduce misclassification errors (misdiagnosis). We propose a novel Sequential Batch Test algorithm that can acquire sets of attribute values in sequence, similar to sets of medical tests ordered by doctors in sequence. The goal of our algorithm is to minimize the total cost (i.e., the trade-off) of acquiring attribute values and misclassifications. We demonstrate the effectiveness of our algorithm, and show that it outperforms previous methods significantly. Our algorithm can be readily applied in real-world diagnosis tasks. A case study on the heart disease is given in the paper.

show abstract

Experience with a model of sequential diagnosis

Cited by 162 publications

References 5 publications

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

Use of health information technology to reduce diagnostic errors

Feature value acquisition in testing

Contact Info

Product

Resources

About