Contrastive Learning-Based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling Using EHRs
Predicting the risk of in-hospital mortality from electronic health records (EHRs) has received
considerable attention. Such predictions will provide early warning of a patient's health …
considerable attention. Such predictions will provide early warning of a patient's health …
Deep Imputation-Prediction Networks for Health Risk Prediction using Electronic Health Records
Electronic health records (EHRs) have an inherently high degree of irregularity, including
many missing values and varying time intervals, due to variations in patient conditions and …
many missing values and varying time intervals, due to variations in patient conditions and …
PATNet: propensity-adjusted temporal network for joint imputation and prediction using binary EHRs with observation bias
Predictive analysis of electronic health records (EHR) is a fundamental task that could
provide actionable insights to help clinicians improve the efficiency and quality of care. EHR …
provide actionable insights to help clinicians improve the efficiency and quality of care. EHR …
Compound density networks for risk prediction using electronic health records
Electronic Health Records (EHRs) exhibit a high amount of missing data due to variations of
patient conditions and treatment needs. Imputation of missing values has been considered …
patient conditions and treatment needs. Imputation of missing values has been considered …
Stochastic imputation and uncertainty-aware attention to EHR for mortality prediction
Electronic health records (EHR) have become an important source of a patient data but
characterized by a variety of missing values. Using the variational inference of Bayesian …
characterized by a variety of missing values. Using the variational inference of Bayesian …
Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction
Analyzing the health status of patients based on Electronic Health Records (EHR) is a
fundamental research problem in medical informatics. The presence of extensive missing …
fundamental research problem in medical informatics. The presence of extensive missing …
[HTML][HTML] Assessing the impact of imputation on the interpretations of prediction models: A case study on mortality prediction for patients with acute myocardial infarction
Acute myocardial infarction poses significant health risks and financial burden on healthcare
and families. Prediction of mortality risk among AM! patients using rich electronic health …
and families. Prediction of mortality risk among AM! patients using rich electronic health …
[HTML][HTML] A deep learning–based, unsupervised method to impute missing values in electronic health records for improved patient management
Electronic health records (EHRs) often suffer missing values, for which recent advances in
deep learning offer a promising remedy. We develop a deep learning–based, unsupervised …
deep learning offer a promising remedy. We develop a deep learning–based, unsupervised …
Integrated convolutional and recurrent neural networks for health risk prediction using patient journey data with many missing values
Predicting the health risks of patients using Electronic Health Records (EHR) has attracted
considerable attention in recent years, especially with the development of deep learning …
considerable attention in recent years, especially with the development of deep learning …
PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations
Electronic Health Records (EHRs) provide valuable patient data but often suffer from
sparsity issue, posing significant challenges in predictive modeling. Conventional imputation …
sparsity issue, posing significant challenges in predictive modeling. Conventional imputation …