{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T05:33:57Z","timestamp":1750224837033},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"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":[[2021,11,18]]},"abstract":"<jats:p>There is a growing trend in building deep learning patient representations from health records to obtain a comprehensive view of a patient\u2019s data for machine learning tasks. This paper proposes a reproducible approach to generate patient pathways from health records and to transform them into a machine-processable image-like structure useful for deep learning tasks. Based on this approach, we generated over a million pathways from FAIR synthetic health records and used them to train a convolutional neural network. Our initial experiments show the accuracy of the CNN on a prediction task is comparable or better than other autoencoders trained on the same data, while requiring significantly less computational resources for training. We also assess the impact of the size of the training dataset on autoencoders performances. The source code for generating pathways from health records is provided as open source.<\/jats:p>","DOI":"10.3233\/shti210800","type":"book-chapter","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T08:58:02Z","timestamp":1637312282000},"source":"Crossref","is-referenced-by-count":2,"title":["Encoding Health Records into Pathway Representations for Deep Learning"],"prefix":"10.3233","author":[{"given":"Marco Luca","family":"Sbodio","sequence":"first","affiliation":[{"name":"IBM Research Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natasha","family":"Mulligan","sequence":"additional","affiliation":[{"name":"IBM Research Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanie","family":"Speichert","sequence":"additional","affiliation":[{"name":"IBM Research Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanessa","family":"Lopez","sequence":"additional","affiliation":[{"name":"IBM Research Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joao","family":"Bettencourt-Silva","sequence":"additional","affiliation":[{"name":"IBM Research Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210800","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T08:58:03Z","timestamp":1637312283000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210800","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,18]]}}}