Publications:Readmission prediction using deep learning on electronic health records
From ISLAB/CAISR
| Title | Readmission prediction using deep learning on electronic health records |
|---|---|
| Author | Awais Ashfaq and Anita Sant'Anna and Markus Lingman and Sławomir Nowaczyk |
| Year | 2019 |
| PublicationType | Journal Paper |
| Journal | Journal of Biomedical Informatics |
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| Conference | |
| DOI | http://dx.doi.org/10.1016/j.jbi.2019.103256 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1308084 |
| Abstract | Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7,500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We show that the model with all key elements achieves a higher discrimination ability (AUC 0.77) compared to the rest. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors |
