文献类型：期刊文献

英文题名：Lab indicators standardization method for the regional healthcare platform: a case study on heart failure

作者：Liang, Ming[1];Zhang, ZhiXing[1];Zhang, JiaYing[1];Ruan, Tong[1];Ye, Qi[1];He, Ping[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Shanghai Hosp Dev Ctr, 2 Kangding Rd, Shanghai 200000, Peoples R China

年份：2020

卷号：20

外文期刊名：BMC MEDICAL INFORMATICS AND DECISION MAKING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000600129600003)】；

基金：The publication cost of this article was funded by the National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant 2019ZX09201004, and design of the study, data interpretation and writing of the manuscript were done under the support of National Key R&D Program of China for "Precision Medical Research" under Grant2018YFC091050.

语种：英文

外文关键词：Lab indicator standardization; Entity alignment; Active learning; Machine learning; Electronic health record; Heart failure

摘要：Background: Laboratory indicator test results in electronic health records have been applied to many clinical big data analysis. However, it is quite common that the same laboratory examination item (i.e., lab indicator) is presented using different names in Chinese due to the translation problem and the habit problem of various hospitals, which results in distortion of analysis results. Methods: A framework with a recall model and a binary classification model is proposed, which could reduce the alignment scale and improve the accuracy of lab indicator normalization. To reduce alignment scale, tf-idf is used for candidate selection. To assure the accuracy of output, we utilize enhanced sequential inference model for binary classification. And active learning is applied with a selection strategy which is proposed for reducing annotation cost. Results: Since our indicator standardization method mainly focuses on Chinese indicator inconsistency, we perform our experiment on Shanghai Hospital Development Center and select clinical data from 8 hospitals. The method achieves a F1-score 92.08% in our final binary classification. As for active learning, the new strategy proposed performs better than random baseline and could outperform the result trained on full data with only 43% training data. A case study on heart failure clinic analysis conducted on the sub-dataset collected from SHDC shows that our proposed method is practical in the application with good performance. Conclusion: This work demonstrates that the structure we proposed can be effectively applied to lab indicator normalization. And active learning is also suitable for this task for cost reduction. Such a method is also valuable in data cleaning, data mining, text extracting and entity alignment.