{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:47:55Z","timestamp":1772754475189,"version":"3.50.1"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Here, we developed a deep neural network to predict future cost from health insurance claims records. We applied the deep network and a ridge regression model to a sample of 1.4 million German insurants to predict total one-year health care costs. Both methods were compared to existing models with various performance measures and were also used to predict patients with a change in costs and to identify relevant codes for this prediction.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We showed that the neural network outperformed the ridge regression as well as all considered models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In summary, we showed that our deep neural network can leverage the full complexity of the patient records and outperforms standard approaches. We suggest that the better performance is due to the ability to incorporate complex interactions in the model and that the model might also be used for predicting other health phenotypes.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01743-z","type":"journal-article","created":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T12:04:01Z","timestamp":1643889841000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep learning for prediction of population health costs"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5459-9771","authenticated-orcid":false,"given":"Philipp","family":"Drewe-Boss","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dirk","family":"Enders","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jochen","family":"Walker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uwe","family":"Ohler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"1743_CR1","doi-asserted-by":"crossref","unstructured":"Sushmita S, Newman S, Marquardt J, Ram P, Prasad V, Cock MD, Teredesai A. Population cost prediction on public healthcare datasets. In: Proceedings of the 5th international conference on digital health 2015\u2014DH 15. ACM Press, 2015.","DOI":"10.1145\/2750511.2750521"},{"issue":"6","key":"1743_CR2","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1287\/opre.1080.0619","volume":"56","author":"D Bertsimas","year":"2008","unstructured":"Bertsimas D, Bjarnad\u00f3ttir MV, Kane MA, Kryder JC, Pandey R, Vempala S, Wang G. Algorithmic prediction of health-care costs. Oper Res. 2008;56(6):1382\u201392.","journal-title":"Oper Res"},{"key":"1743_CR3","unstructured":"Lahiri B, Agarwal N. Predicting healthcare expenditure increase for an individual from medicare data. In: Proceedings of the ACM SIGKDD workshop on health informatics, 2014."},{"key":"1743_CR4","unstructured":"Dr\u00f6sler S, Garbe E, Hasford J, Schubert I, Ulrich V, van\u00a0de Ven W, Wambach A, Wasem J, Wille E. Sondergutachten zu den wirkungen des morbidit\u00e4tsorientierten risikostrukturausgleichs. Bonn, Wissenschaftlicher Beirat zur Weiterentwicklung des Risikostrukturausgleichs beim Bundesversicherungsamt im Auftrag des Bundesministeriums f\u00fcr Gesundheit, 2017."},{"issue":"2","key":"1743_CR5","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1017\/S1748499512000346","volume":"7","author":"EW Frees","year":"2013","unstructured":"Frees EW, Jin X, Lin X. Actuarial applications of multivariate two-part regression models. Ann Actuarial Sci. 2013;7(2):258\u201387.","journal-title":"Ann Actuarial Sci"},{"issue":"1","key":"1743_CR6","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/10920277.2015.1110491","volume":"20","author":"I Duncan","year":"2016","unstructured":"Duncan I, Loginov M, Ludkovski M. Testing alternative regression frameworks for predictive modeling of health care costs. 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