加護病房患者臨床結果預測──機器學習與主題模型法之應用

Objectives: Predicting clinical patients' vital signs remains a critical issue in intensive care unit (ICU) related studies. However, studies on electronic health record (EHR) data have mostly analyzed numerical data and rarely semi-structured textual data. Methods: Our study used structured and semi-structured data (i.e., patients' diagnosis data and inspection reports) collected from the MIMIC-III database. First, we used the Latent Dirichlet Allocation (LDA) model (a model employed in natural language processing) to process semi-structured data. Then, we used machine learning methods for the prediction of clinical outcomes in 38,597 adult ICU patients. Results: Based on the results, combining the structured and semi-structured data of ICU patients can strengthen the ICU patient mortality prediction accuracy. The model with machine learning methods generated favorable mortality predictions, where the highest AUROC, for long-term mortality is 0.871, and the highest AUROC for short-term mortality is 0.922. Conclusions: The constructed model successfully identified crucial variables for predicting patient mortality. Thus, when providing medical services to patients, health care personnel may consider the critical variables associated with the patients' hospitalization durations to ensure that the patients receive optimal medical services.