Automatic symptom name normalization in clinical records of traditional Chinese medicine

Abstract

BackgroundIn recent years, Data Mining technology has been applied more than ever before in the field of traditional Chinese medicine (TCM) to discover regularities from the experience accumulated in the past thousands of years in China. Electronic medical records (or clinical records) of TCM, containing larger amount of information than well-structured data of prescriptions extracted manually from TCM literature such as information related to medical treatment process, could be an important source for discovering valuable regularities of TCM. However, they are collected by TCM doctors on a day to day basis without the support of authoritative editorial board, and owing to different experience and background of TCM doctors, the same concept might be described in several different terms. Therefore, clinical records of TCM cannot be used directly to Data Mining and Knowledge Discovery. This paper focuses its attention on the phenomena of "one symptom with different names" and investigates a series of metrics for automatically normalizing symptom names in clinical records of TCM.ResultsA series of extensive experiments were performed to validate the metrics proposed, and they have shown that the hybrid similarity metrics integrating literal similarity and remedy-based similarity are more accurate than the others which are based on literal similarity or remedy-based similarity alone, and the highest F-Measure (65.62%) of all the metrics is achieved by hybrid similarity metric VSM+TFIDF+SWD.ConclusionsAutomatic symptom name normalization is an essential task for discovering knowledge from clinical data of TCM. The problem is introduced for the first time by this paper. The results have verified that the investigated metrics are reasonable and accurate, and the hybrid similarity metrics are much better than the metrics based on literal similarity or remedy-based similarity alone.

Efficacy-specific herbal group detection from traditional Chinese medicine prescriptions via hierarchical attentive neural network model

“…In recent years, many disciplines witness rapid growth in employing data-intensive approaches based on machine learning and data mining technologies to discover patterns hidden in a massive volume of data. Likewise, some efforts have emerged in TCM recently which utilize machine learning and data mining technologies for discovering knowledge from TCM literature [ 15 – 18 ], clinical records [ 8 , 19 , 20 ], and prescriptions [ 12 , 13 , 21 – 26 ]. Among all these data mining tasks the last one draws particular attention due to the fact that, prescriptions as the primary knowledge sources for TCM are invented mostly with empirical experiences in a long historical span of times and distilling knowledge from them are far from completion, which hinders applications of TCM in modern society.…”

Network-based drug discovery by integrating systems biology and computational technologies

“…Third, the terminology and topology of herbal medicine have not been standardized and the herbal databases are not all open-accessed and thus could not be integrated into the interlinked data with the public available databases [83]. For examples, many herbals are termed with different languages depending on different countries, thus direct extraction of structured data from biomedical texts cannot be achieved [84, 85]. The difference in ontology/taxonomy of herbal which sometimes causes misunderstanding and confusion in application, and may cause serious drug poisoning due to misuse of the herb [86].…”

Multi‐label feature selection with application to TCM state identification

“…Zhou et al proposed a new data warehouse system, which stored various information entities and clinical relationships from the clinical practices in TCM. Wang et al designed a number of algorithms to normalize the clinical symptom name to the most similar standard form by quantifying the similarity between the clinical symptom name and all feasible standard forms. Chen et al established a new database of multi‐herb/western drug interactions and studied the interactions of TCM and western drugs by the database.…”