Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

Chih-Chou Chiu, Chung-Min Wu, Te-Nien Chien (), Ling-Jing Kao, Chengcheng Li and Chuan-Mei Chu
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Chih-Chou Chiu: Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
Chung-Min Wu: Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
Te-Nien Chien: College of Management, National Taipei University of Technology, Taipei 106, Taiwan
Ling-Jing Kao: Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
Chengcheng Li: College of Management, National Taipei University of Technology, Taipei 106, Taiwan
Chuan-Mei Chu: College of Management, National Taipei University of Technology, Taipei 106, Taiwan

IJERPH, 2023, vol. 20, issue 5, 1-22

Abstract: An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient’s age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

Keywords: structured vs. unstructured data; machine learning; intensive care units; electronic health records; predictive modeling (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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