{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T17:31:53Z","timestamp":1778434313904,"version":"3.51.4"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2021,9,30]]},"abstract":"<jats:p> The adoption of deep learning in healthcare is hindered by their \u201cblack box\u201d nature. In this paper, we explore the RETAIN architecture for the task of glucose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB and the type-1 OhioT1DM datasets by comparing its statistical and clinical performances against two deep models and three models based on decision trees. We show that the RETAIN model offers a very good compromise between accuracy and interpretability, being almost as accurate as the LSTM and FCN models while remaining interpretable. We show the usefulness of its interpretable nature by analyzing the contribution of each variable to the final prediction. It revealed that signal values older than 1[Formula: see text]h are not used by the RETAIN model for 30[Formula: see text]min ahead of time prediction of glucose. Also, we show how the RETAIN model changes its behavior upon the arrival of an event such as carbohydrate intakes or insulin infusions. In particular, it showed that the patient\u2019s state before the event is particularly important for the prediction. Overall the RETAIN model, thanks to its interpretability, seems to be a very promising model for regression or classification tasks in healthcare. <\/jats:p>","DOI":"10.1142\/s0218001421600065","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T15:59:57Z","timestamp":1631894397000},"source":"Crossref","is-referenced-by-count":7,"title":["Enhancing the Interpretability of Deep Models in Healthcare Through Attention: Application to Glucose Forecasting for Diabetic People"],"prefix":"10.1142","volume":"35","author":[{"given":"Maxime","family":"De Bois","sequence":"first","affiliation":[{"name":"CNRS-LIMSI and Universit\u00e9 Paris-Saclay, Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moun\u00eem A.","family":"El Yacoubi","sequence":"additional","affiliation":[{"name":"Samovar, CNRS, T\u00e9l\u00e9com SudParis, Institut Polytechnique de Paris, \u00c9vry, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Ammi","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris 8, Saint-Denis, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"S0218001421600065BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"S0218001421600065BIB002","first-page":"2654","volume-title":"Advances in Neural Information Processing Systems","author":"Ba J.","year":"2014"},{"key":"S0218001421600065BIB004","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2017.0387"},{"key":"S0218001421600065BIB005","first-page":"3504","volume-title":"Advances in Neural Information Processing Systems","author":"Choi E.","year":"2016"},{"key":"S0218001421600065BIB008","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59830-3_59"},{"key":"S0218001421600065BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105874"},{"issue":"3","key":"S0218001421600065BIB010","first-page":"1","volume":"1341","author":"Erhan D.","year":"2009","journal-title":"Univ. 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