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Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers\u2019 rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.<\/jats:p>","DOI":"10.1111\/jnp.12244","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T15:38:01Z","timestamp":1617032281000},"page":"319-339","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?"],"prefix":"10.1111","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4061-5152","authenticated-orcid":false,"given":"Emma","family":"Mawdsley","sequence":"first","affiliation":[{"name":"Mental Health and Wellbeing Institute of Health and Wellbeing University of Glasgow UK"},{"name":"NHS Greater Glasgow and Clyde UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bronagh","family":"Reynolds","sequence":"additional","affiliation":[{"name":"Mental Health and Wellbeing Institute of Health and Wellbeing University of Glasgow UK"},{"name":"NHS Ayrshire and Arran UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Breda","family":"Cullen","sequence":"additional","affiliation":[{"name":"Mental Health and Wellbeing Institute of Health and Wellbeing University of Glasgow UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,3,29]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1249\/MSS.0000000000001903"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_8_4_1","volume-title":"Critical appraisal tools:Checklist for cohort studies","author":"Briggs J.","year":"2017"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4526"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2019.02.004"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2016.4819"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_2_8_9_1","volume-title":"Handbook of research on enterprise 2.0: Technological Social, and organizational dimensions: technological, social, and organizational dimensions","author":"Cruz\u2010Cunha M. 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