BACKGROUND
There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer wait times. The triage process plays a crucial role in assessing and stratifying patients' risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation.
OBJECTIVE
In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHR) and machine learning.
METHODS
To achieve this objective, we will conduct a retrospective, single-centre study based on a large, longitudinal dataset obtained from the EHR of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit, inpatient death, among others. With pre-identified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning-based AutoScore to develop three SERT scores. These three scores can be used at different times in the ED, i.e., upon arrival, during the ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. The receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation.
RESULTS
The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022.
CONCLUSIONS
The SERT scoring system proposed in this study will be unique and innovative due to its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools.
Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation (Preprint)