ABSTRACT
A new method for diagnosing multiple diseases in large medical decision support systems based on causal probabilistic networks is proposed. The method is based on characteristics of the diagnostic process that we believe to be present in many diagnostic tasks, both inside and outside medicine. The diagnosis must often be made under uncertainty, choosing between diagnoses that each have small prior probabilities, but not so small that the possibility of two or more simultaneous diseases can be ignored. Often a symptom can be caused by several diseases and the presence of several diseases tend to aggravate the symptoms. For diagnostic problems that share these characteristic, we have proposed a method that operates in a number of phases: in the first phase only single diseases are considered and this helps to focus the attention on a smaller number of plausible diseases. In the second phase, pairs of diseases are considered, which make it possible to narrow down the field of plausible diagnoses further. In the following phases, larger subsets of diseases are considered. The method was applied to the diagnosis of neuromuscular disorders, using previous experience with the so-called MUNIN system as a starting point. The results showed that the method gave large reductions in computation time without compromising the computational accuracy in any substantial way. It is concluded that the method enables practical inference in large medical expert systems based on causal probabilistic networks.
Subject(s)
Diagnosis, Computer-Assisted , Neural Networks, Computer , Neuromuscular Diseases/diagnosis , Decision Trees , Diagnosis, Differential , False Negative Reactions , False Positive Reactions , Humans , Muscular Diseases/complications , Muscular Diseases/diagnosis , Peripheral Nervous System Diseases/complications , Peripheral Nervous System Diseases/diagnosis , Time FactorsABSTRACT
There is a large difference between the prevalence of a given disease in the general population and in the population seen in the EMG lab. It can be argued that both prevalences are the correct choice as prior probabilities for the diseases. This paradox is resolved by recognizing that the EMG diagnosis is only based on the information provided by the EMG examination and thus only represents a partial view of the patient. We propose a solution summarizing the set of findings, signs and symptoms, lab results etc., that led to the referral of the patient for an EMG examination. This information is described by stochastic variables called FIDL factors (Found In Doctor's Lab). The approach is tested on the EMG expert system MUNIN with 30 previously evaluated cases. The results show that this solution improves the specificity of the diagnosis, without affecting the sensitivity.
Subject(s)
Diagnosis, Computer-Assisted/methods , Electromyography/methods , Decision Support Systems, Clinical/instrumentation , Decision Support Systems, Clinical/statistics & numerical data , Diagnosis, Computer-Assisted/instrumentation , Diagnosis, Computer-Assisted/statistics & numerical data , Electromyography/instrumentation , Electromyography/statistics & numerical data , Expert Systems/instrumentation , False Negative Reactions , False Positive Reactions , Humans , Median Neuropathy/diagnosis , Sensitivity and Specificity , Stochastic Processes , Ulnar Neuropathies/diagnosisABSTRACT
This paper describes the work undertaken to establish principles for the development of multicenter databases for reference values in clinical neurophysiology. The study was initiated because of interest of the involved laboratories in knowledge-based systems in electromyographic diagnosis, for which it was necessary to formalize the key concepts in the diagnostic process: diseases, pathophysiology and test results. The paper deals specifically with the structuring of results of motor and sensory nerve conduction studies.
