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Through automatic extraction, transformation and integration of follow-up data in the real world such as demography, treatment, and the disease course, a chronic database of patient status is established. In combination with age-period-cohort, logistic regression and Cox models, we apply the AutoAHP to assess disease risk and implement risk prediction in practice.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A list of essential factors for risk prediction are identified, including annual changes in mental health policy, public support, regional difference, patient gender, compliance, and social function. After the verification of 1,222,038 complete disease course and treatment records of 256,050 patients, the AutoAHP framework achieves a precision of 0.923, a recall of 0.924, and a F1 of 0.923. The model is demonstrated to be superior to general models and has better performance in risk prediction.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Aiming at the risk assessment of patients with schizophrenia which is influenced by factors, such as time, region and complication, the AutoAHP framework is able to be applied as a model in combination with logistic regression and Cox models to support clinical analysis of disease risk related factors and assist decision-making in chronic disease management.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01749-1","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T12:03:10Z","timestamp":1641902590000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Disease risk analysis for schizophrenia patients by an automatic AHP framework"],"prefix":"10.1186","volume":"21","author":[{"given":"Wenyan","family":"Tan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5203-8663","authenticated-orcid":false,"given":"Heng","family":"Weng","sequence":"additional","affiliation":[]},{"given":"Haicheng","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Aihua","family":"Ou","sequence":"additional","affiliation":[]},{"given":"Zehui","family":"He","sequence":"additional","affiliation":[]},{"given":"Fujun","family":"Jia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"issue":"1","key":"1749_CR1","doi-asserted-by":"publisher","DOI":"10.7189\/jogh.05.010412","volume":"5","author":"KY Chan","year":"2015","unstructured":"Chan KY, Zhao FF, et al. 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