{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:56Z","timestamp":1747216196129,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Rare diseases pose significant challenges due to their heterogeneity and lack of knowledge. This study develops a comprehensive pipeline interoperable with a document-oriented clinical data warehouse, integrating cohort characterization, patient clustering and interpretation. Leveraging NLP, semantic similarity, machine learning and visualization, the pipeline enables the identification of prevalent phenotype patterns and patient stratification. To enhance interpretability, discriminant phenotypes characterizing each cluster are provided. Users can visually test hypotheses by marking patients exhibiting specific keywords in the EHR like genes, drugs and procedures. Implemented through a web interface, the pipeline enables clinicians to navigate through different modules, discover intricate patterns and generate interpretable insights that may advance rare diseases understanding, guide decision-making, and ultimately improve patient outcomes.<\/jats:p>","DOI":"10.3233\/shti240777","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:43:00Z","timestamp":1724409780000},"source":"Crossref","is-referenced-by-count":0,"title":["An Integrated Pipeline for Phenotypic Characterization, Clustering and Visualization of Patient Cohorts in a Rare Disease-Oriented Clinical Data Warehouse"],"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 Paris Cit\u00e9, Inserm UMR 1163, Paris, France"},{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Data Science Platform, Imagine Institute, Universit\u00e9 Paris Cit\u00e9, Inserm UMR 1163, 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 Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"},{"name":"Universit\u00e9 Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 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 Paris Cit\u00e9, Inserm UMR 1163, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosy","family":"Tsopra","sequence":"additional","affiliation":[{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anita","family":"Burgun","sequence":"additional","affiliation":[{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"},{"name":"Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, D\u00e9partement d\u2019informatique m\u00e9dicale, Assistance Publique-H\u00f4pitaux de Paris (AP-HP), Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[{"name":"Data Science Platform, Imagine Institute, Universit\u00e9 Paris Cit\u00e9, Inserm UMR 1163, Paris, France"},{"name":"Inserm, Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"HeKA, Inria Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240777","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:43:01Z","timestamp":1724409781000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240777","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}