title: Data-Driven Information Extraction from Chinese Electronic Medical Records.
abstract: OBJECTIVE
  This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event.
  
  
  METHODS
  Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions.
  
  
  RESULTS
  The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846.
  
  
  CONCLUSIONS
  In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838).
  
  
  CONCLUSIONS
  The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica.
authors: Department of Computer Science & Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China.
authors: Dong
authors: D
authors: Xu
authors: R & D information China, AstraZeneca, 199 Liangjing Road, Pudong, Shanghai, 201203, China.
authors: Meizhuo
authors: M
authors: Zhang
authors: Department of Computer Science & Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China.
authors: Tianwan
authors: T
authors: Zhao
authors: Department of Computer Science & Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China.
authors: Chen
authors: C
authors: Ge
authors: R & D information China, AstraZeneca, 199 Liangjing Road, Pudong, Shanghai, 201203, China.
authors: Weiguo
authors: W
authors: Gao
authors: R & D information China, AstraZeneca, 199 Liangjing Road, Pudong, Shanghai, 201203, China.
authors: Jia
authors: J
authors: Wei
authors: Department of Computer Science & Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China.
authors: Kenny Q
authors: KQ
authors: Zhu
mesh_terms: D000465:Algorithms; D002681:China; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D009323:Natural Language Processing; D039061:Systematized Nomenclature of Medicine