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However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and\/or low back pain.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and\/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science methods for data prepossessing and evaluate selected regression and classification methods for predicting patient outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results show that there is a potential for machine learning to predict and classify PROMs. The prediction models based on baseline measurements perform well, and the number of predictors can be reduced, which is an advantage for implementation in decision support scenarios. The classification task shows that the dataset does not contain all necessary predictors for the care type classification. Overall, the work presents generalizable machine learning pipelines that can be adapted to other PROMs datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study demonstrates the potential of PROMs in predicting short-term patient outcomes. Our results indicate that machine learning methods can be used to exploit the predictive value of PROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-022-01973-9","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T11:03:06Z","timestamp":1662030186000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes"],"prefix":"10.1186","volume":"22","author":[{"given":"Deepika","family":"Verma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duncan","family":"Jansen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kerstin","family":"Bach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mannes","family":"Poel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul Jarle","family":"Mork","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wendy Oude Nijeweme","family":"d\u2019Hollosy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"issue":"4","key":"1973_CR1","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1136\/svn-2017-000101","volume":"2","author":"F Jiang","year":"2017","unstructured":"Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. 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In: Proceedings of the 13th international joint conference on biomedical engineering systems and technologies (BIOSTEC 2020), vol. 5: HEALTHINF; 2020, p. 117\u2013124. https:\/\/doi.org\/10.5220\/0008962101170124.","DOI":"10.5220\/0008962101170124"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-01973-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T02:26:14Z","timestamp":1727922374000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-01973-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,1]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["1973"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-01973-9","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,1]]},"assertion":[{"value":"18 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Missing Open Access funding information has been added in the Funding Note","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Ethics approvals were obtained for both <scp>self<\/scp>BACK and Back-UP before the collection of data. Approval for the <scp>self<\/scp>BACK trial was obtained from the Regional Ethics Committees for Medical and Health Research Ethics in Denmark (S-20182000-24) and Norway (2018\/791). Approval from the data protection agency was obtained for Denmark (18\/17955) through the University of Southern Denmark. For the Back-UP trial, approval was obtained from the Regional Committee for Medical and Health Research Ethics in Central Norway (Ref. 2019\/64084). All participants provided written informed consent that their data collected as part of the trial can be used in research. No formal ethical approval was required for data collected by Roessingh Center of Rehabilitation since according to the Dutch law (Medical Research Involving Human Subjects Act), the nature of this research did not require formal medical ethical approval. Access to the data was granted by an employee of the Roessingh Center of Rehabilitation who is responsible for managing the dataset, with the verbal agreement of not sharing the data with anyone else. At the beginning of the questionnaire, patients were asked whether they agreed to the use of their data for research purposes (informed consent), where they had to tick a checkbox if they agreed.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All participants provided written informed consent acknowledging that their data may be used in research, results of which may be made public, but personal identifiable information will not be disclosed in any publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"227"}}