J Biomed Inform - Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: an empirical study.

Tópicos

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Resumo

Clinical records of traditional Chinese medicine (TCM) are documented by TCM doctors during their routine diagnostic work. These records contain abundant knowledge and reflect the clinical experience of TCM doctors. In recent years, with the modernization of TCM clinical practice, these clinical records have begun to be digitized. Data mining (DM) and machine learning (ML) methods provide an opportunity for researchers to discover TCM regularities buried in the large volume of clinical records. There has been some work on this problem. Existing methods have been validated on a limited amount of manually well-structured data. However, the contents of most fields in the clinical records are unstructured. As a result, the previous methods verified on the well-structured data will not work effectively on the free-text clinical records (FCRs), and the FCRs are, consequently, required to be structured in advance. Manually structuring the large volume of TCM FCRs is time-consuming and labor-intensive, but the development of automatic methods for the structuring task is at an early stage. Therefore, in this paper, symptom name recognition (SNR) in the chief complaints, which is one of the important tasks to structure the FCRs of TCM, is carefully studied. The SNR task is reasonably treated as a sequence labeling problem, and several fundamental and practical problems in the SNR task are studied, such as how to adapt a general sequence labeling strategy for the SNR task according to the domain-specific characteristics of the chief complaints and which sequence classifier is more appropriate to solve the SNR task. To answer these questions, a series of elaborate experiments were performed, and the results are explained in detail.

Resumo Limpo

clinic record tradit chines medicin tcm document tcm doctor routin diagnost work record contain abund knowledg reflect clinic experi tcm doctor recent year modern tcm clinic practic clinic record begun digit data mine dm machin learn ml method provid opportun research discov tcm regular buri larg volum clinic record work problem exist method valid limit amount manual wellstructur data howev content field clinic record unstructur result previous method verifi wellstructur data will work effect freetext clinic record fcrs fcrs consequ requir structur advanc manual structur larg volum tcm fcrs timeconsum laborintens develop automat method structur task earli stage therefor paper symptom name recognit snr chief complaint one import task structur fcrs tcm care studi snr task reason treat sequenc label problem sever fundament practic problem snr task studi adapt general sequenc label strategi snr task accord domainspecif characterist chief complaint sequenc classifi appropri solv snr task answer question seri elabor experi perform result explain detail

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