{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:48:56Z","timestamp":1747216136966,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643683881"},{"type":"electronic","value":"9781643683898"}],"license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"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":[[2023,5,18]]},"abstract":"<jats:p>In the context of medical concept extraction, it is critical to determine if clinical signs or symptoms mentioned in the text were present or absent, experienced by the patient or their relatives. Previous studies have focused on the NLP aspect but not on how to leverage this supplemental information for clinical applications. In this paper, we aim to use the patient similarity networks framework to aggregate different phenotyping modalities. NLP techniques were applied to extract phenotypes and predict their modalities from 5470 narrative reports of 148 patients with ciliopathies (a group of rare diseases). Patient similarities were computed using each modality separately for aggregation and clustering. We found that aggregating negated phenotypes improved patient similarity, but further aggregating relatives\u2019 phenotypes worsened the result. We suggest that different modalities of phenotypes can contribute to patient similarity, but they should be aggregated carefully and with appropriate similarity metrics and aggregation models.<\/jats:p>","DOI":"10.3233\/shti230342","type":"book-chapter","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T04:49:54Z","timestamp":1684471794000},"source":"Crossref","is-referenced-by-count":1,"title":["Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-5158","authenticated-orcid":false,"given":"Xiaoyi","family":"Chen","sequence":"first","affiliation":[{"name":"Data Science Platform, Imagine Institute, Universit\u00e9 de Paris Cit\u00e9, Inserm UMR 1163, Paris, France"},{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 de Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carole","family":"Faviez","sequence":"additional","affiliation":[{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 de Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Vincent","sequence":"additional","affiliation":[{"name":"Data Science Platform, Imagine Institute, Universit\u00e9 de Paris Cit\u00e9, Inserm UMR 1163, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"Saunier","sequence":"additional","affiliation":[{"name":"Laboratory of Renal Hereditary Diseases, Imagine Institute, Universit\u00e9 de Paris Cit\u00e9, Inserm UMR 1163, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[{"name":"Data Science Platform, Imagine Institute, Universit\u00e9 de Paris Cit\u00e9, Inserm UMR 1163, Paris, France"},{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 de Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anita","family":"Burgun","sequence":"additional","affiliation":[{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 de Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, D\u00e9partement d\u2019informatique m\u00e9dicale, Assistance Publique-H\u00f4pitaux de Paris (AP-HP), Paris, France"},{"name":"PaRis Artificial Intelligence Research InstitutE (PRAIRIE), France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Caring is Sharing \u2013 Exploiting the Value in Data for Health and Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T11:03:58Z","timestamp":1685531038000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"ISBN":["9781643683881","9781643683898"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230342","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}