Temporal Phenotype Matrix Engineering for Electronic Health Records - Enhancing Coronary Artery Disease Prediction

Kuan-Hui Liu; Cheng-Yu Chiang; Hsin-Yao Wang; Yi-Ju Tseng

Abstract

Electronic health records (EHRs) often exhibit sparsity and irregularity due to their inherent nature. It is crucial to consider that imputation and aggregation techniques used during EHRs preprocessing can introduce artificial and unrealistic data and potentially leading to the loss of critical information. In this study, we proposed a temporal phenotype matrix engineering approach with auxiliary data layer (ADL) to extract important hidden information from EHRs. Our proposed approach was applied to the early prediction of coronary artery disease (CAD), one of the leading causes of death worldwide. We evaluated the performance of the LSTM, CNN, and TCN models on the CAD prediction task. Upon applying our proposed matrix engineering technique with ADL, we observed a substantial improvement, with an AUROC (area under the receiver operating characteristic) score of 0.919 ± 0.006 (a 10% increase, compared to when no ADL was included, 0.831 ± 0.011) in CNN model. In conclusion, this study highlights the benefits of the proposed temporal phenotype matrix engineering approach with ADL to address the sparsity and irregularity inherent in EHRs data. Our findings underscore the potential of the proposed temporal phenotype matrix engineering approach with ADL for enhancing the early prediction of CAD, thereby contributing to improved patient outcomes and reduced mortality rates.

Keywords: time-series prediction; electronic health records; time-series data; coronary artery disease

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