Abstract Electronic health record (EHR) systems are used extensively throughout the healthcare domain. However, data interchangeability between EHR systems is limited due to the use of different coding standards across systems. Existing methods of mapping coding standards based on manual human experts mapping, dictionary mapping, symbolic NLP and classification are unscalable and cannot accommodate large …

Text2Node: a cross-domain system for mapping arbitrary phrases to a taxonomy Read More »

Abstract Introduction: Deep learning has outperformed traditional methods in predicting healthcare outcomes. However, deep learning models struggle with explainability and are considered a black box. This article demonstrated the output from Shapley additive explanations (SHAP) analysis can provide meaningful insight into a model’s predictions. Methods: Starting from Taiwan National Health Insurance Research Database, we selected …

Explainable model of deep learning for outcomes prediction of in-hospital cardiac arrest patients Read More »

Abstract Background: In-hospital cardiac arrest (IHCA) is associated with high mortality and health care costs in the recovery phase. Predicting adverse outcome events, including readmission, improves the chance for appropriate interventions and reduces health care costs. However, studies related to the early prediction of adverse events of IHCA survivors are rare. Therefore, we used a …

Multi-task learning improves model performance in predicting rare catastrophic events in healthcare claims dataset Read More »