Extended Topic Model Based Abnormal Medical Prescription Detection Method

Abnormal prescriptions refer to abnormal prescriptions prescribed by doctors for patients. Abnormalities in medical prescription, such as drug abuse and incorrect medicine, will affect the efficiency of treatment and even result in serious consequence. Because of some subjective or objective reasons, there exist more or less abnormal prescriptions. The detection of the abnormal prescriptions can improve the efficiency of the patient??s medical treat-ment and reduce the cost of the medical treatment. It is of great significance to drug abuse, polypharmacy and incorrect medicine. For this reason, this paper proposes an extended topic model based abnormal medical prescri-ption detection method, which can automatically detect the abnormal prescription from a large number of prescri-ption data. In addition, for each new prescription, this method can determine whether its diagnose and medicine match, and then determine whether it is normal or not. Compared with other abnormal detection algorithms, this method has a wider range of applications, not only can be used to detect abnormal prescriptions, but also can be used to detect the matching relations between other features. This method has been implemented and its validity has been verified in a real prescription dataset.

Abstract licence: CC BY-NC

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