{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/retain-an-interpretable-predictive-model-for","title":"RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism","arxiv_id":"1608.05745","date":"2016-08-19","proceeding":"NeurIPS 2016 12","authors":["Edward Choi","Mohammad Taha Bahadori","Joshua A. Kulas","Andy Schuetz","Walter F. Stewart","Jimeng Sun"],"abstract":"Accuracy and interpretability are two dominant features of successful\npredictive models. Typically, a choice must be made in favor of complex black\nbox models such as recurrent neural networks (RNN) for accuracy versus less\naccurate but more interpretable traditional models such as logistic regression.\nThis tradeoff poses challenges in medicine where both accuracy and\ninterpretability are important. We addressed this challenge by developing the\nREverse Time AttentIoN model (RETAIN) for application to Electronic Health\nRecords (EHR) data. RETAIN achieves high accuracy while remaining clinically\ninterpretable and is based on a two-level neural attention model that detects\ninfluential past visits and significant clinical variables within those visits\n(e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR\ndata in a reverse time order so that recent clinical visits are likely to\nreceive higher attention. RETAIN was tested on a large health system EHR\ndataset with 14 million visits completed by 263K patients over an 8 year period\nand demonstrated predictive accuracy and computational scalability comparable\nto state-of-the-art methods such as RNN, and ease of interpretability\ncomparable to traditional models.","url_abs":"http://arxiv.org/abs/1608.05745v4","url_pdf":"http://arxiv.org/pdf/1608.05745v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"retain-an-interpretable-predictive-model-for","repo_url":"https://github.com/mp2893/retain","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"disease-trajectory-forecasting","task_name":"Disease Trajectory Forecasting"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/disease-trajectory-forecasting-on-uk-cf-trust","task":"Disease Trajectory Forecasting","dataset":"UK CF trust","model":"RETAIN","rank_in_archive_order":2,"of":3,"metrics":{"AUC (ABPA)":"0.685","AUC (Aspergillus)":"0.641","AUC (Diabetes)":"0.764","AUC (E. Coli)":"0.697","AUC (I. Obstruction)":"0.578","AUC (K. Pneumonia)":"0.715","I. Obstruction":"0.578"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.05745","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}