Exploring data quality for machine learning-based disease risk predictions with electronic medical records_Chinese Journal of Evidence-Based Medicine

Authors：

DUAN Yifan , TANG Mingkun , SUN Haixia , HAO Jie , WANG Jiayang , ZHOU Jiayin , LI Jiao ,  QIAN Qing

Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, P. R. China;

Corresponding author：

QIAN Qing, Email: qian.qing@imicams.ac.cn

Keywords：

Disease risk prediction; Data quality; Demand models; Electronic medical records

DOI：

10.7507/1672-2531.202301076

Video：

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Objective To construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. Methods Referring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. Results We constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. Conclusion The proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.

Citation： DUAN Yifan, TANG Mingkun, SUN Haixia, HAO Jie, WANG Jiayang, ZHOU Jiayin, LI Jiao, QIAN Qing. Exploring data quality for machine learning-based disease risk predictions with electronic medical records. Chinese Journal of Evidence-Based Medicine, 2023, 23(9): 1072-1080. doi: 10.7507/1672-2531.202301076 Copy

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1. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol, 2017, 2(4): 230-243.
2. 谷鸿秋, 王俊峰, 章仲恒, 等. 临床预测模型: 模型的建立. 中国循证心血管医学杂志, 2019, 11(1): 14-16, 23.
3. 王士泉, 苏明亮, 高东平. 基于大数据的青光眼眼压实时预警监测系统研究. 中国数字医学, 2018, 13(5): 5-7, 15.
4. 徐怡. 复杂疾病的风险预测模型研究及遗传变异数据库挖掘. 杭州: 浙江大学, 2019.
5. 李言生, 龚后武, 栗翊超, 等. 基于真实世界数据的疾病风险预测研究. 医学信息, 2020, 33(23): 17-19.
6. Rudrapatna VA, Butte AJ. Opportunities and challenges in using real-world data for health care. J Clin Invest, 2020, 130(2): 565-574.
7. Johnson SG, Speedie S, Simon G, et al. A data quality ontology for the secondary use of EHR data. AMIA Annu Symp Proc, 2015, 5: 1937-1946.
8. 余炜伦. 电子病历中的数据质量与病人群组可视分析. 杭州: 浙江大学, 2020.
9. 杨鑫禹, 牟冬梅, 彭浩, 等. 基于数据特征的电子病历数据驱动临床决策模型研究. 情报理论与实践, 2022, 45(5): 181-188.
10. Ni K, Chu H, Zeng L, et al. Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study. BMJ Open, 2019, 9(7): e029314.
11. 田琪, 陈雅妮, 韩喆僖, 等. 临床数据质量评估指标研究. 医学信息学杂志, 2020, 41(10): 9-17.
12. Weiskopf NG, Bakken S, Hripcsak G, et al. A data quality assessment guideline for electronic health record data reuse. EGEMS (Wash DC), 2017, 5(1): 14.
13. 熊兴江. 医疗大数据质量评价指标体系构建研究. 武汉: 华中科技大学, 2019.
14. Spjuth O, Frid J, Hellander A. The machine learning life cycle and the cloud: implications for drug discovery. Expert Opin Drug Discov, 2021, 16(9): 1071-1079.
15. Eric H. Machine learning life cycle. 2021.
16. Srcmini. 机器学习开发的生命周期. 2021.
17. 国家药品监督管理局医疗器械技术审评中心. 人工智能医疗器械注册审查指导原则. 2022.
18. 国家药品监督管理局. 深度学习辅助决策医疗器械软件审评要点. 2022.
19. 昝松亭. 移动医疗大数据的数据质量评估模型研究. 北京: 北京邮电大学, 2019.
20. 江心怡, 陈敏. 电子病历数据治理方法的研究. 中国医院管理, 2020, 40(8): 68-70.
21. Sun Y, Lu T, Gu N. A method of electronic health data quality assessment: enabling data provenance. Wellington: 2017 IEEE 21st international conference on computer supported cooperative work in design (CSCWD), 2017: 233-238.
22. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc, 2013, 20(1): 144-151.
23. Kahn MG, Callahan TJ, Barnard J, et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. EGEMS (Wash DC), 2016, 4(1): 1244.
24. 胡丹青. 基于电子病历的急性冠脉综合征患者主要不良心血管事件预测. 杭州: 浙江大学, 2017.
25. Le S, Allen A, Calvert J, et al. Convolutional neural network model for intensive care unit acute kidney injury prediction. Kidney Int Rep, 2021, 6(5): 1289-1298.
26. 李冬冬. 脑卒中电子病历文本数据结构化方法研究及应用. 上海: 中国人民解放军海军军医大学, 2021.
27. Dinh A, Miertschin S, Young A, et al. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak, 2019, 19(1): 211.
28. Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
29. He J, Hu Y, Zhang X, et al. Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records. JAMIA Open, 2019, 2(1): 115-122.
30. 杜珍珍. 基于电子病历的机器学习算法在心血管疾病预测方面的应用. 武汉: 武汉邮电科学研究院, 2020.
31. Li Y, Chen X, Shen Z, et al. Prediction models for acute kidney injury in patients with gastrointestinal cancers: a real-world study based on Bayesian networks. Ren Fail, 2020, 42(1): 869-876.
32. 肖文翔. 基于电子病历分析的糖尿病患病风险数据挖掘方法研究. 青岛: 青岛大学, 2016.
33. 吴风浪, 周铭, 耿鹏. 医院电子病历的多维数据分析研究. 中国医学装备, 2019, 16(9): 132-135.
34. Wang H, Tan X, Huang Z, et al. Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks. Artif Intell Med, 2020, 105: 101859.
35. 王萍. 基于电子病历数据的疾病预测模型构建研究. 长春: 吉林大学, 2017.
36. 杨玉洁. 基于医学大数据的心血管慢病风险精准建模. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2021.
37. Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia, 2021, 64(7): 1504-1515.
38. 陈德华, 周东阳, 乐嘉锦. 基于深度学习的甲状腺结节良恶性预测方法研究. 微型机与应用, 2017, 36(12): 13-15.
39. 李星彦. 基于电子病历的辅助问诊系统的设计与实现. 北京: 北京邮电大学, 2020.
40. 袁莎, 沈丽宁. 医疗数据质量评价指标体系研究. 卫生软科学, 2020, 34(12): 12-16.

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