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Many techniques utilized in modeling diseases are often in the form of a \u201cblack box\u201d where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model\/pattern, known as a \u201clatent phenotype,\u201d with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' \ndata.<\/jats:p>","DOI":"10.1111\/coin.12313","type":"journal-article","created":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T02:40:30Z","timestamp":1585536030000},"page":"1460-1498","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1952-0674","authenticated-orcid":false,"given":"Leila","family":"Yousefi","sequence":"first","affiliation":[{"name":"Department of Computer Science Brunel University London  London UK"}]},{"given":"Stephen","family":"Swift","sequence":"additional","affiliation":[{"name":"Department of Computer Science Brunel University London  London UK"}]},{"given":"Mahir","family":"Arzoky","sequence":"additional","affiliation":[{"name":"Department of Computer Science Brunel University London  London UK"}]},{"given":"Lucia","family":"Saachi","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Pavia  Pavia Italy"}]},{"given":"Luca","family":"Chiovato","sequence":"additional","affiliation":[{"name":"Unit of Endocrinology University of Pavia  Pavia Italy"}]},{"given":"Allan","family":"Tucker","sequence":"additional","affiliation":[{"name":"Department of Computer Science Brunel University London  London UK"}]}],"member":"311","published-online":{"date-parts":[[2020,3,29]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"YousefiL SaachiL BellazziR ChiovatoL TuckerA. 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