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In general, computational phenotype discovery aims to find subgroups among individuals that share distinctive characteristics by analyzing electronic health records (EHR). This can benefit the understanding of a disease as well as uncover risk factors and provide possibilities for preventive action. The features in the<jats:italic>women<\/jats:italic>(<jats:inline-formula><jats:alternatives><jats:tex-math>$$n = 6359$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>n<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn>6359<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>) by<jats:italic>questionnaire features<\/jats:italic>(<jats:inline-formula><jats:alternatives><jats:tex-math>$$p=29$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>p<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn>29<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>) matrix with missing data are of different statistical data types (e.g., binary or ordinal data). We use so-called<jats:italic>generalized low-rank models<\/jats:italic>(GLRM) that can address this challenge via different statistical-data-type-dependent loss functions. We show that these models can uncover phenotypes related to cervical cancer risk factors from large-scale questionnaire data.<\/jats:p>","DOI":"10.1007\/978-3-031-17030-0_8","type":"book-chapter","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T13:40:37Z","timestamp":1675258837000},"page":"94-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Phenotyping of\u00a0Cervical Cancer Risk Groups via\u00a0Generalized Low-Rank Models Using Medical Questionnaires"],"prefix":"10.1007","author":[{"given":"Florian","family":"Becker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mari","family":"Nyg\u00e5rd","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Nyg\u00e5rd","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Age","family":"Smilde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evrim","family":"Acar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103125","volume":"93","author":"I Perros","year":"2019","unstructured":"Perros, I., Papalexakis, E.E., Vuduc, R., Searles, E., Sun, J.: Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization. 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