Engineering and Information Technology Collected Works - Research Publications
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ItemEnhancing Predictive Modeling in Emergency Departments(SCITEPRESS - Science and Technology Publications, 2024)Increasing global Emergency Department (ED) visits, exacerbated by COVID-19, has presented multiple challenges in recent years. Electronic Health Records (EHRs) as comprehensive digital repositories of patient health information offer a pathway to construct prediction systems to address these issues. However, the heterogeneity of EHRs complicates accurate predictions. A notable challenge is the prevalence of high-cardinality nominal features (NFs) in EHRs. Due to their numerous distinct values, these features are often excluded from the analysis, risking information loss, reduced accuracy, and interpretability. This study proposes a framework, integrating a preprocessing technique with target encoding (TE-PrepNet) into machine learning (ML) models to address challenges of NFs from MIMIC-IV-ED. We evaluate performance of TE-PrepNet in two specific ED-based prediction tasks: triage-based hospital admissions and ED reattendance within 72 hours at discharge time. Incorporating three NFs, our approach demonstrates improvements compared to the baseline and outperforms previous research that overlooked NFs. Random forest model with TE-PrepNet in the prediction of hospitalisation achieved an AUROC of 0.8458, compared to the baseline AUROC of 0.7520. For the prediction of ED reattendance within 72 hours, the utilisation of XGBoost yielded an improvement, attaining an AUROC of 0.6975, outperforming the baseline AUROC of 0.6166.