{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:11:08Z","timestamp":1774552268377,"version":"3.50.1"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p><jats:bold>Aim:<\/jats:bold> To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques.<\/jats:p><jats:p><jats:bold>Method:<\/jats:bold> The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009\u20132013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation. Three different stroke types [intracerebral hemorrhage (ICH), ischemic stroke, transient ischemic attack (TIA)] were considered and five different comparison outcome settings were considered. Our electronic administrative record based predictive model was compared with a predictive model composed of \u201cbaseline\u201d clinical features, more specific for stroke, such as age, gender, smoking habits, co-morbidities (high cholesterol, hypertension, atrial fibrillation, and ischemic heart disease), types of imaging done (CT scan, MRI, etc.), and occurrence of in-hospital pneumonia. Risk factors associated with likelihood of negative outcomes were identified.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The data was highly reliable at predicting discharge to rehabilitation and all other outcomes vs. death for ICH (AUC 0.85 and 0.825, respectively), all discharge outcomes except home vs. rehabilitation for ischemic stroke, and discharge home vs. others and home vs. rehabilitation for TIA (AUC 0.948 and 0.873, respectively). Electronic health record data appeared to provide improved prediction of outcomes over stroke specific clinical factors from the machine learning models. Common risk factors associated with a negative impact on expected outcomes appeared clinically intuitive, and included older age groups, prior ventilatory support, urinary incontinence, need for imaging, and need for allied health input.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.<\/jats:p>","DOI":"10.3389\/fneur.2021.670379","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T16:39:34Z","timestamp":1632760774000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records"],"prefix":"10.3389","volume":"12","author":[{"given":"Santu","family":"Rana","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Truyen","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Talman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanh","family":"Phan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Clissold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/1471-2458-11-86","article-title":"Utility of electronic patient records in primary care for stroke secondary prevention trials","volume":"11","author":"Dregan","year":"2011","journal-title":"BMC Public Health."},{"key":"B2","unstructured":"Victorian Admitted Episodes Dataset: Department of Health and Human Services, State Government of Victoria2015"},{"key":"B3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1177\/1460458209353559","article-title":"Determining patient characteristics for decision analysis support systems using anonymized electronic patient records","volume":"16","author":"Frisher","year":"2010","journal-title":"Health Informatics J."},{"key":"B4","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3389\/fneur.2017.00180","article-title":"Stroke incidence in Victoria, Australia\u2014emerging improvements","volume":"8","author":"Clissold","year":"2017","journal-title":"Front Neurol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1161\/01.STR.30.10.2105","article-title":"Trends in the incidence, severity, and short-term outcome of stroke in perth, Western Australia","volume":"30","author":"Jamrozik","year":"1999","journal-title":"Stroke."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1056\/NEJM199512143332401","article-title":"Tissue plasminogen activator for acute ischemic stroke","volume":"333","year":"1995","journal-title":"N Engl J Med."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1016\/S0140-6736(16)00163-X","article-title":"Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials","volume":"387","author":"Goyal","year":"2016","journal-title":"Lancet."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1056\/NEJMoa0804656","article-title":"Thrombolysis with alteplase 3 to 45 hours after acute ischemic stroke","volume":"359","author":"Hacke","year":"2008","journal-title":"N Engl J Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"CD000197","DOI":"10.1002\/14651858.CD000197.pub3","article-title":"Organised inpatient (stroke unit) care for stroke","volume":"2013","year":"2013","journal-title":"Cochrane Database Syst Rev."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1111\/j.1538-7836.2005.01427.x","article-title":"Preventable stroke and stroke prevention","volume":"3","author":"Hankey","year":"2005","journal-title":"J Thromb Haemost."},{"key":"B11","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1159\/000085543","article-title":"Early supported discharge for stroke patients improves clinical outcome. 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