Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: An empirical study

Abstract

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.

Enriching the international clinical nomenclature with Chinese daily used synonyms and concept recognition in physician notes

“…Another two similar experiments, which focused on clinical finding, achieved the F-score of 0.91 [42] and 0.92 [43] respectively. The most comparable study, carried out by Wang on traditional Chinese medicine, showed a better performance (F-score of 0.951) than the results presented here [25]. One reason for the difference might be that traditional Chinese medicine record is more rigid than present days.…”

“…Besides, the terminology-based recognition methods were popular if the dictionaries (such as SCT or UMLS) are easy to include [23, 24]. For lacking of effective Chinese clinical nomenclature, Wang [25] compared three models (the CRF, HMM and MEMM) for the task of symptom name recognition in traditional Chinese clinical records. It also indicated that the CRF outperforms the other two methods.…”

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

“…Various learning models and techniques have been widely used by NER researchers, such as the conditional random field (CRF) models (Liu & Zhou, 2013;Majumder et al, 2012), support vector machine (SVM) models (Saha et al, 2010), hidden Markov models (HMMs; Wang et al, 2014), logistic expression models (Ekbal & Saha, 2011), and maximum entropy Markov models (Saha et al, 2010). Various learning models and techniques have been widely used by NER researchers, such as the conditional random field (CRF) models (Liu & Zhou, 2013;Majumder et al, 2012), support vector machine (SVM) models (Saha et al, 2010), hidden Markov models (HMMs; Wang et al, 2014), logistic expression models (Ekbal & Saha, 2011), and maximum entropy Markov models (Saha et al, 2010).…”

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text

“…So far, little effort has been devoted to NER in ANs written in logographic languages, especially in Chinese. As reported, several ML-based algorithms including CRF, SVM, and maximum entropy have been used to identify symptom descriptions and pathogenic mechanisms from TCM EMRs [19]. Also, CRF has been used to identify biomedical name entities in Chinese research abstracts [20].…”