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This subtype is characterized by more rapid cognitive decline compared to AD patients without psychosis. Therefore, there is a great need to identify risk factors for the development of AD + P and explore potential treatment options. In this study, we enhanced our deep learning model, DeepBiomarker, to predict the onset of psychosis in AD utilizing data from electronic medical records (EMRs). The model demonstrated superior predictive capacity with an AUC (area under curve) of 0.907, significantly surpassing conventional risk prediction models. Utilizing a perturbation-based method, we identified key features from multiple medications, comorbidities, and abnormal laboratory tests, which notably influenced the prediction outcomes. Our findings demonstrated substantial agreement with existing studies, underscoring the vital role of metabolic syndrome, inflammation, and liver function pathways in AD + P. Importantly, the DeepBiomarker model not only offers a precise prediction of AD + P onset but also provides mechanistic understanding, potentially informing the development of innovative treatments. With additional validation, this approach could significantly contribute to early detection and prevention strategies for AD + P, thereby improving patient outcomes and quality of life.<\/jats:p>","DOI":"10.3390\/ph16070911","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T02:29:18Z","timestamp":1687400958000},"page":"911","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer\u2019s Disease through Electronic Medical Records with Deep Learning Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9814-5085","authenticated-orcid":false,"given":"Peihao","family":"Fan","sequence":"first","affiliation":[{"name":"Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oshin","family":"Miranda","sequence":"additional","affiliation":[{"name":"Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0325-2118","authenticated-orcid":false,"given":"Xiguang","family":"Qi","sequence":"additional","affiliation":[{"name":"Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Kofler","sequence":"additional","affiliation":[{"name":"Department of Pathology, Division of Neuropathology, UPMC Presbyterian Hospital, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert A.","family":"Sweet","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA"},{"name":"Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lirong","family":"Wang","sequence":"additional","affiliation":[{"name":"Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2016). 2016 Alzheimer\u2019s disease facts and figures. 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