{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T09:05:18Z","timestamp":1769504718184,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Precision Driven Health","award":["PDH#603"],"award-info":[{"award-number":["PDH#603"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s11517-020-02165-1","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T11:03:41Z","timestamp":1587639821000},"page":"1459-1466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A machine learning model for predicting risk of hospital readmission within 30\u00a0days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model"],"prefix":"10.1007","volume":"58","author":[{"given":"Mirza Mansoor","family":"Baig","sequence":"first","affiliation":[]},{"given":"Ning","family":"Hua","sequence":"additional","affiliation":[]},{"given":"Edmond","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Reece","family":"Robinson","sequence":"additional","affiliation":[]},{"given":"Anna","family":"Spyker","sequence":"additional","affiliation":[]},{"given":"Delwyn","family":"Armstrong","sequence":"additional","affiliation":[]},{"given":"Robyn","family":"Whittaker","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Robinson","sequence":"additional","affiliation":[]},{"given":"Ehsan","family":"Ullah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"issue":"17","key":"2165_CR1","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1001\/jama.2010.533","volume":"303","author":"AF Hernandez","year":"2010","unstructured":"Hernandez AF, Greiner MA, Fonarow GC, Hammill BG, Heidenreich PA, Yancy CW, Peterson ED, Curtis LH (2010) Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. Jama 303(17):1716\u20131722","journal-title":"Jama"},{"issue":"6","key":"2165_CR2","doi-asserted-by":"publisher","first-page":"E12","DOI":"10.1002\/jhm.822","volume":"5","author":"PK Lindenauer","year":"2010","unstructured":"Lindenauer PK, Bernheim SM, Grady JN, Lin Z, Wang Y, Wang Y, Merrill AR, Han LF, Rapp MT, Drye EE, Normand SL, Krumholz HM (2010) The performance of US hospitals as reflected in risk-standardized 30-day mortality and readmission rates for Medicare beneficiaries with pneumonia. J Hosp Med 5(6):E12\u2013E18","journal-title":"J Hosp Med"},{"issue":"1422","key":"2165_CR3","first-page":"13","volume":"128","author":"T Blakely","year":"2015","unstructured":"Blakely T et al (2015) Updated New Zealand health system cost estimates from health events by sex, age and proximity to death: further improvements in the age of \u2018big data\u2019. N Z Med J 128(1422):13\u201323","journal-title":"N Z Med J"},{"issue":"7295","key":"2165_CR4","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1136\/bmj.322.7295.1171","volume":"322","author":"N Devlin","year":"2001","unstructured":"Devlin N, Maynard A, Mays N (2001) New Zealand\u2019s new health sector reforms: back to the future? BMJ: Br Med J 322(7295):1171","journal-title":"BMJ: Br Med J"},{"issue":"12","key":"2165_CR5","doi-asserted-by":"publisher","first-page":"e173949","DOI":"10.1001\/jamasurg.2017.3949","volume":"152","author":"AM Ali","year":"2017","unstructured":"Ali AM et al (2017) Factors associated with 30-day readmission after primary total hip arthroplasty: analysis of 514 455 procedures in the UK National Health Service. JAMA Surg 152(12):e173949\u2013e173949","journal-title":"JAMA Surg"},{"issue":"1","key":"2165_CR6","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1186\/s12882-017-0729-9","volume":"18","author":"N Jurawan","year":"2017","unstructured":"Jurawan N, Pankhurst T, Ferro C, Nightingale P, Coleman J, Rosser D, Ball S (2017) Hospital acquired acute kidney injury is associated with increased mortality but not increased readmission rates in a UK acute hospital. BMC Nephrol 18(1):317","journal-title":"BMC Nephrol"},{"key":"2165_CR7","unstructured":"Rumball-Smith J, Hider P (2009) The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. N Z Med J(Online) 122(1289)"},{"key":"2165_CR8","doi-asserted-by":"publisher","unstructured":"Shulan M, Gao K, Moore CD (2013) Predicting 30-day all-cause hospital readmissions. Health Care Manag Sci 16(2):167\u2013175. https:\/\/doi.org\/10.1007\/s10729-013-9220-8","DOI":"10.1007\/s10729-013-9220-8"},{"key":"2165_CR9","unstructured":"Yam C et al (2010) Measuring and preventing potentially avoidable hospital readmissions: a review of the literature. Hong Kong Medical Journal = Xianggang yi xue za zhi 16(5):383\u2013389"},{"issue":"15","key":"2165_CR10","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1001\/jama.2011.1515","volume":"306","author":"D Kansagara","year":"2011","unstructured":"Kansagara D et al (2011) Risk prediction models for hospital readmission: a systematic review. Jama 306(15):1688\u20131698","journal-title":"Jama"},{"issue":"2","key":"2165_CR11","first-page":"19","volume":"11","author":"JJ Holloway","year":"1989","unstructured":"Holloway JJ, Thomas JW (1989) Factors influencing read mission risk: implications for quality monitoring. Health Care Financ Rev 11(2):19\u201332","journal-title":"Health Care Financ Rev"},{"issue":"1","key":"2165_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-017-0580-8","volume":"18","author":"Y Maali","year":"2018","unstructured":"Maali Y, Perez-Concha O, Coiera E, Roffe D, Day RO, Gallego B (2018) Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital. BMC Med Inform Decis Mak 18(1):1","journal-title":"BMC Med Inform Decis Mak"},{"issue":"6","key":"2165_CR13","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1503\/cmaj.091117","volume":"182","author":"C van Walraven","year":"2010","unstructured":"van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ (2010) Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Can Med Assoc J 182(6):551\u2013557","journal-title":"Can Med Assoc J"},{"issue":"4","key":"2165_CR14","doi-asserted-by":"publisher","first-page":"e001667","DOI":"10.1136\/bmjopen-2012-001667","volume":"2","author":"J Billings","year":"2012","unstructured":"Billings J et al (2012) Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30). BMJ Open 2(4):e001667","journal-title":"BMJ Open"},{"key":"2165_CR15","volume-title":"A model for predicting readmission risk in New Zealand","author":"R Vaithianathan","year":"2012","unstructured":"Vaithianathan R, Jiang N, Ashton T (2012) A model for predicting readmission risk in New Zealand. AUT University, Faculty of Business and Law"},{"key":"2165_CR16","doi-asserted-by":"crossref","unstructured":"Ross, S., et al. Learning message-passing inference machines for structured prediction. in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. 2011. IEEE","DOI":"10.1109\/CVPR.2011.5995724"},{"issue":"1","key":"2165_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar A et al (2018) Scalable and accurate deep learning with electronic health records. NPJ Digit Med 1(1):18","journal-title":"NPJ Digit Med"},{"issue":"2\u20133","key":"2165_CR18","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1023\/A:1007601113994","volume":"39","author":"D Freitag","year":"2000","unstructured":"Freitag D (2000) Machine learning for information extraction in informal domains. Mach Learn 39(2\u20133):169\u2013202","journal-title":"Mach Learn"},{"issue":"1","key":"2165_CR19","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"2165_CR20","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"2165_CR21","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"7","key":"2165_CR22","doi-asserted-by":"publisher","first-page":"1128","DOI":"10.1007\/s00134-016-4349-9","volume":"42","author":"M Paul","year":"2016","unstructured":"Paul M, Bougouin W, Geri G, Dumas F, Champigneulle B, Legriel S, Charpentier J, Mira JP, Sandroni C, Cariou A (2016) Delayed awakening after cardiac arrest: prevalence and risk factors in the Parisian registry. Intensive Care Med 42(7):1128\u20131136","journal-title":"Intensive Care Med"},{"issue":"3","key":"2165_CR23","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.ajo.2014.11.032","volume":"159","author":"K Vaziri","year":"2015","unstructured":"Vaziri K, Pershing S, Albini TA, Moshfeghi DM, Moshfeghi AA (2015) Risk factors predictive of endogenous endophthalmitis among hospitalized patients with hematogenous infections in the United States. Am J Ophthalmol 159(3):498\u2013504","journal-title":"Am J Ophthalmol"},{"issue":"1","key":"2165_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/acem.12569","volume":"22","author":"CR Carpenter","year":"2015","unstructured":"Carpenter CR, Shelton E, Fowler S, Suffoletto B, Platts-Mills TF, Rothman RE, Hogan TM (2015) Risk factors and screening instruments to predict adverse outcomes for undifferentiated older emergency department patients: a systematic review and meta-analysis. Acad Emerg Med 22(1):1\u201321","journal-title":"Acad Emerg Med"},{"issue":"4","key":"2165_CR25","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1192\/bjp.bp.115.170050","volume":"209","author":"MK Chan","year":"2016","unstructured":"Chan MK, Bhatti H, Meader N, Stockton S, Evans J, O'Connor RC, Kapur N, Kendall T (2016) Predicting suicide following self-harm: systematic review of risk factors and risk scales. Br J Psychiatry 209(4):277\u2013283","journal-title":"Br J Psychiatry"},{"key":"2165_CR26","doi-asserted-by":"publisher","first-page":"k497","DOI":"10.1136\/bmj.k497","volume":"360","author":"JG Berry","year":"2018","unstructured":"Berry JG, Gay JC, Maddox KJ, Coleman EA, Bucholz EM, O\u2019Neill MR, Blaine K, Hall M (2018) Age trends in 30 day hospital readmissions: US national retrospective analysis. BMJ 360:k497","journal-title":"BMJ"},{"issue":"11","key":"2165_CR27","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1097\/MLR.0000000000000435","volume":"53","author":"GJ Escobar","year":"2015","unstructured":"Escobar GJ, Ragins A, Scheirer P, Liu V, Robles J, Kipnis P (2015) Nonelective rehospitalizations and postdischarge mortality: predictive models suitable for use in real time. Med Care 53(11):916","journal-title":"Med Care"},{"issue":"6","key":"2165_CR28","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1111\/jep.12012","volume":"19","author":"C van Walraven","year":"2013","unstructured":"van Walraven C, Wong J, Forster AJ, Hawken S (2013) Predicting post-discharge death or readmission: deterioration of model performance in population having multiple admissions per patient. J Eval Clin Pract 19(6):1012\u20131018","journal-title":"J Eval Clin Pract"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02165-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-020-02165-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02165-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T23:26:05Z","timestamp":1619133965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-020-02165-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,23]]},"references-count":28,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["2165"],"URL":"https:\/\/doi.org\/10.1007\/s11517-020-02165-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,23]]},"assertion":[{"value":"30 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}