Lab indicators standardization method for the regional healthcare platform: a case study on heart failure
- PDF / 3,919,854 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 1 Downloads / 172 Views
Open Access
RESEARCH
Lab indicators standardization method for the regional healthcare platform: a case study on heart failure Ming Liang1, ZhiXing Zhang1, JiaYing Zhang1, Tong Ruan1*†, Qi Ye1 and Ping He2
From 10th International Workshop on Biomedical and Health Informatics San Diego, CA, USA. 18-20 November 2019
Abstract 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. Keywords: Lab indicator standardization, Entity alignment, Active learning, Machine learning, Electronic health record, Heart failure
*Correspondence: [email protected] † Equal contributor 1 School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China Full list of author information is available at the end of the article
Background Electronic health records (EHRs) have been applied to many clinical data analysis, such as prognostic analysis and decision support. In EHRs, laboratory indicator test results are considered to be important factors. For example, “ ” (Aspartate aminotransferase, AST) can be regarded as a diagnostic
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you g
Data Loading...
