Improving Chinese electronic medical record retrieval by field weight assignment, negation detection, and re-ranking

• Published in: Journal of biomedical informatics Vol. 119; p. 103836
• Main Authors: Yang, Songchun, Zheng, Xiangwen, Xiao, Yu, Yin, Xiangfei, Pang, Jianfei, Mao, Huajian, Wei, Wei, Zhang, Wenqin, Yang, Yu, Xu, Haifeng, Li, Mei, Zhao, Dongsheng
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2021
• Subjects: Chinese electronic medical record; Field weight; Information retrieval; Negation detection; Pseudo relevance feedback; Re-ranking; Information retrieval; Field weight; Chinese electronic medical record; Pseudo relevance feedback; Negation detection; Re-ranking
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2021.103836

[Display omitted] •The field weights of Chinese EMRs are assigned to enhance the clinical significance of EMR retrieval.•Negative relations are detected to get accurate retrieval scores.•The original retrieval results are re-ranked by expansion terms and time information. The technique of information retrieval has been widely used in electronic medical record (EMR) systems. It’s a pity that most existing methods have not considered the structures and language features of Chinese EMRs, which affects the performance of retrieval. To improve accuracy and comprehensiveness, we propose an improved algorithm of Chinese EMR retrieval. First, the weights of fields in Chinese EMRs are assigned based on the corresponding importance in clinical applications. Second, negative relations in EMRs are detected, and the retrieval scores of negative terms are adjusted accordingly. Third, the retrieval results are re-ranked by expansion terms and time information to enhance the recall without decreasing precision. Experiment results show that the improved algorithm increases the precision and recall significantly, which shows that the algorithm takes a full account of the characteristics of Chinese EMRs and fits the needs for clinical applications.