| 摘要 |
A system and method for estimating and updating the current state of disease of a patient, called the disease impression. The disease impression of the patient, for a disease with a set of possible disease conditions, is updated based upon the current disease impression, the outcome of a test performed with respect to the disease in the patient, and the conditional probabilities of obtaining the test outcome given each of the disease conditions for the disease. The conditional probabilities with respect to all possible outcomes of the test and conditions of the disease may be stored in a likelihood matrix, which comprises a medical knowledge base. Because tests are error prone, the estimate of the patient's state of disease evolves according to a hidden Markov model, which allows the clinical impression of the patient to be computed in real-time. By applying the method to many diseases and many tests, large-scale medical diagnosis is achieved and is computationally tractable because as a result of the process, such diagnosis becomes stochastic filtering problem that is trivially parallel. In addition, the expected diagnostic utility of any test can be predicted. The same knowledge base is used to perform both inference and prediction.
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