{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:09:58Z","timestamp":1766178598831},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Bidirectional recurrent neural networks (RNN) improved performance of various natural language processing tasks and recently have been used for diagnosis prediction. Advantages of general bidirectional RNN, however, are not readily applied to diagnosis prediction task. In this study, we present a simple way to efficiently apply bidirectional RNN for diagnosis prediction without using any additional networks or parameters.<\/jats:p>","DOI":"10.3233\/shti220264","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:34:56Z","timestamp":1654594496000},"source":"Crossref","is-referenced-by-count":1,"title":["Towards Better Diagnosis Prediction Using Bidirectional Recurrent Neural Networks"],"prefix":"10.3233","author":[{"given":"Junghwan","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Casey","family":"Ta","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhua","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220264","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:34:57Z","timestamp":1654594497000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220264","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}