Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study

Abstract

Background The widespread secondary use of electronic medical records (EMRs) promotes health care quality improvement. Representation learning that can automatically extract hidden information from EMR data has gained increasing attention. Objective We aimed to propose a patient representation with more feature associations and task-specific feature importance to improve the outcome prediction performance for inpatients with acute myocardial infarction (AMI). Methods Medical concepts, including patients’ age, gender, disease diagnoses, laboratory tests, structured radiological features, procedures, and medications, were first embedded into real-value vectors using the improved skip-gram algorithm, where concepts in the context windows were selected by feature association strengths measured by association rule confidence. Then, each patient was represented as the sum of the feature embeddings weighted by the task-specific feature importance, which was applied to facilitate predictive model prediction from global and local perspectives. We finally applied the proposed patient representation into mortality risk prediction for 3010 and 1671 AMI inpatients from a public data set and a private data set, respectively, and compared it with several reference representation methods in terms of the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. Results Compared with the reference methods, the proposed embedding-based representation showed consistently superior predictive performance on the 2 data sets, achieving mean AUROCs of 0.878 and 0.973, AUPRCs of 0.220 and 0.505, and F1-scores of 0.376 and 0.674 for the public and private data sets, respectively, while the greatest AUROCs, AUPRCs, and F1-scores among the reference methods were 0.847 and 0.939, 0.196 and 0.283, and 0.344 and 0.361 for the public and private data sets, respectively. Feature importance integrated in patient representation reflected features that were also critical in prediction tasks and clinical practice. Conclusions The introduction of feature associations and feature importance facilitated an effective patient representation and contributed to prediction performance improvement and model interpretation.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study

“…A substantial effort has been devoted to developing models for predicting mortality in patients with AMI [5]. In the past, generalized linear models (eg, the logistic regression [LR] model and Cox proportional hazard model) have been used to predict mortality in patients with AMI [6][7][8][9]. However, the generalized linear models fail to capture the nonlinear relations of the massive, high-dimensional, and incomplete medical data, which hinder the practical use and further clinical application of the models [10].…”

“…In addition, while a complex model may perform better than a simpler one, it could lead to black-box problems, meaning that only the inputs and outputs of the model can be seen and it is difficult to understand how the inputs affect the predictions, which limit their clinical acceptance and raise ethical and legal questions [18]. The proposal of methods that could provide explanations for black-box models might facilitate clinicians in understanding predictions and making faster and more accurate treatment decisions [9,19,20]. At present, there is a lack of tools to predict in-hospital mortality of patients with AMI based on tabular data-specific deep learning models, and the potential of state-of-the-art deep learning models for predicting mortality in AMI is unclear.…”