Kirby SE, Dennis SM, Jayasinghe UW, Harris MF. Patient-related factors in frequent readmissions: the influence of condition, access to services and patient choice. BMC Health Serv Res. 2010;10:216 Available from: https://pubmed.ncbi.nlm.nih.gov/20663141/ [cited 3 Jan 2022].
Curiati PK, Gil-Junior LA, Morinaga CV, Ganem F, Curiati JAE, Avelino-Silva TJ. Predicting hospital admission and prolonged length of stay in older adults in the emergency department: the PRO-AGE scoring system. Ann Emerg Med. 2020;76(3):255–65. https://doi.org/10.1016/j.annemergmed.2020.01.010.
Nyweide DJ, Anthony DL, Bynum JPW, Strawderman RL, Weeks WB, Casalino LP, et al. Continuity of care and the risk of preventable hospitalization in older adults. JAMA Intern Med. 2013;173(20):1879–85 Available from: https://pubmed.ncbi.nlm.nih.gov/24043127/ [cited 3 Jan 2022].
Strom JB, Kramer DB, Wang Y, Shen C, Wasfy JH, Landon BE, et al. Short-term rehospitalization across the spectrum of age and insurance types in the United States. PLoS One. 2017;12(7):1–12.
Fehlings MG, Tetreault L, Nater A, Choma T, Harrop J, Mroz T, et al. The aging of the global population: the changing epidemiology of disease and spinal disorders. Neurosurgery. 2015;77(4):S1–5.
Picco L, Achilla E, Abdin E, Chong SA, Vaingankar JA, McCrone P, et al. Economic burden of multimorbidity among older adults: impact on healthcare and societal costs. BMC Health Serv Res. 2016;16(1):1–12. https://doi.org/10.1186/s12913-016-1421-7.
Mahmoudi E, Kamdar N, Kim N, Gonzales G, Singh K, Waljee AK. Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review. BMJ. 2020;369:1–10.
Hao S, Wang Y, Jin B, Shin AY, Zhu C, Huang M, et al. Development, validation and deployment of a real time 30 day hospital readmission risk assessment tool in the Maine healthcare information exchange. PLoS One. 2015;8(10):1–15.
Jamei M, Nisnevich A, Wetchler E, Sudat S, & Liu E. Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PloS One. 2017;12(7):e0181173. https://dx.plos.org/10.1371/journal.pone.0181173.
Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):1–10. https://doi.org/10.1038/s41746-018-0029-1.
Zolbanin HM, Delen D. Processing electronic medical records to improve predictive analytics outcomes for hospital readmissions. Decis Support Syst. 2018;112:98–110. https://doi.org/10.1016/j.dss.2018.06.010.
Roimi M, Gutman R, Somer J, Ben Arie A, Calman I, Bar-Lavie Y, et al. Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: a nationwide study. J Am Med Inform Assoc. 2021;28(6):1188–96.
Romero-Brufau S, Whitford D, Johnson MG, Hickman J, Morlan BW, Therneau T, et al. Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic early warning score (MC-EWS). J Am Med Inform Assoc. 2021;28(6):1207–15.
Huang Y, Talwar A, Chatterjee S, Aparasu RR. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol. 2021;21(1):1–14 Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01284-z [cited 10 Jan 2022].
Bagg S, Pombo AP, Hopman W. Effect of age on functional outcomes after stroke rehabilitation. Stroke. 2002;33(1):179–85.
Tan SY, Low LL, Yang Y, Lee KH. Applicability of a previously validated readmission predictive index in medical patients in Singapore: a retrospective study. BMC Health Serv Res. 2013;13:366 Available from: http://www.ncbi.nlm.nih.gov/pubmed/24074454 [cited 6 Nov 2018].
Sarijaloo FB, Park J, Zhong X, Wokhlu A. Predicting 90 day acute heart failure readmission and death using machine learning-supported decision analysis. Clin Cardiol. 2021;44(2):230–7.
Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol. 2017;2(2):204–9.
Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019;95:27–37.
Min X, Yu B, Wang F. Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: a case study on COPD. Sci Rep. 2019;9(1):1–10. https://doi.org/10.1038/s41598-019-39071-y.
Lohman MC, Scherer EA, Whiteman KL, Greenberg RL, Bruce ML. Factors associated with accelerated hospitalization and re-hospitalization among Medicare home health patients. J Gerontol A Biol Sci Med Sci. 2018;73(9):1280–6.
O’Leary KJ, Chapman MM, Foster S, O’Hara L, Henschen BL, Cameron KA. Frequently hospitalized patients’ perceptions of factors contributing to high hospital use. J Hosp Med. 2019;14(9):521–6.
Wu J, Grannis SJ, Xu H, Finnell JT. A practical method for predicting frequent use of emergency department care using routinely available electronic registration data. BMC Emerg Med. 2016;16(1):1–9. https://doi.org/10.1186/s12873-016-0076-3.
Vojta CL, Vojta DD, Tenhave TR, Amaya M, Lavizzo-Mourey R, Asch DA. Risk screening in a Medicare/Medicaid population: administrative data versus self report. J Gen Intern Med. 2001;16(8):525–30.
Longman JM, I Rolfe M, Passey MD, Heathcote KE, Ewald DP, Dunn T, et al. Frequent hospital admission of older people with chronic disease: a cross-sectional survey with telephone follow-up and data linkage. BMC Health Serv Res. 2012;12(1):1–13.
Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41:811–7.
Coleman EA, Wagner EH, Grothaus LC, Hecht J, Savarino J, Buchner DM. Predicting hospitalization and functional decline in older health plan enrollees: are administrative data as accurate as self-report? J Am Geriatr Soc. 1998;46(4):419–25.
Shelton P, Sager MA, Schraeder C. The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit. Am J Manag Care. 2000;6(8):925–33.
Op het Veld LPM, Beurskens AJHM, de Vet HCW, van Kuijk SMJ, Hajema KJ, Kempen GIJM, et al. The ability of four frailty screening instruments to predict mortality, hospitalization and dependency in (instrumental) activities of daily living. Eur J Ageing. 2019;16(3):387–94. https://doi.org/10.1007/s10433-019-00502-4.
Theou O, Sluggett JK, Bell JS, Lalic S, Cooper T, Robson L, et al. Frailty, hospitalization, and mortality in residential aged care. J Gerontol A Biol Sci Med Sci. 2018;73(8):1090–6.
Liang YD, Zhang YN, Li YM, Chen YH, Xu JY, Liu M, et al. Identification of frailty and its risk factors in elderly hospitalized patients from different wards: a cross-sectional study in China. Clin Interv Aging. 2019;14:2249–59.
Lyon D, Lancaster GA, Taylor S, Dowrick C, Chellaswamy H. Predicting the likelihood of emergency admission to hospital of older people: development and validation of the emergency admission risk likelihood index (EARLI). Fam Pract. 2007;24(2):158–67.
Jensen GL, Friedmann JM, Coleman CD, Smiciklas-wright H. Screening for hospitalization and nutritional risks among community-dwelling older persons. Am J Clin Nutr. 2001;74(2):5–9.
Mosley DG, Peterson E, Martin DC. Do hierarchical condition category model scores predict hospitalization risk in newly enrolled medicare advantage participants as well as probability of repeated admission scores? J Am Geriatr Soc. 2009;57(12):2306–10.
Wagner JT, Bachmann LM, Boult C, Harari D, Von Renteln-Kruse W, Egger M, et al. Predicting the risk of hospital admission in older persons - validation of a brief self-administered questionnaire in three European countries. J Am Geriatr Soc. 2006;54(8):1271–6.
O’Caoimh R, Gao Y, Svendrovski A, Healy E, O’Connell E, O’Keeffe G, et al. The risk instrument for screening in the community (RISC): a new instrument for predicting risk of adverse outcomes in community dwelling older adults. BMC Geriatr. 2015;15(1):1–9.
Mazzaglia G, Roti L, Corsini G, Ferrucci A, Bari and M Di. Screening of older community-dwelling people at risk for death and hospitalization: the Assistenza socio-sanitaria in Italia project. J Am Geriatr Soc. 2007;55(12):1955–60.
Canadian Institute for Health Information. Early identification of people at-risk of hospitalization. 2013. Available from: https://secure.cihi.ca/free_products/HARP_reportv_En.pdf.
Tan BY, Gu JY, Wei HY, Chen L, Yan SL, Deng N. Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure. BMC Med Inform Decis Mak. 2019;19(1):1–9.
Zhou H, Della PR, Roberts P, Goh L, Dhaliwal SS. Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review. BMJ Open. 2016;6(6):e011060.
Sutter T, Roth JA, Chin-Cheong K, Hug BL, Vogt JE. A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. J Am Med Inform Assoc. 2021;28(4):868–73.
Kohavi R. Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid. KDD; 1996.
Zhou ZH, Chen ZQ. Hybrid decision tree. Knowledge-Based Syst. 2002;15(8):515–28 Available from: http://www.sciencedirect.com/science/article/pii/S0950705102000382 [cited 6 Dec 2017].
Fox MT, Persaud M, Maimets I, Brooks D, O’Brien K, Tregunno D. Effectiveness of early discharge planning in acutely ill or injured hospitalized older adults: a systematic review and meta-analysis. BMC Geriatr. 2013;13(1):1 Available from: BMC Geriatrics.
Mutai H, Furukawa T, Araki K, Misawa K, Hanihara T. Long-term outcome in stroke survivors after discharge from a convalescent rehabilitation ward. Psychiatry Clin Neurosci. 2013;67(6):434–40.
Szekendi MK, Vaughn J, Lal A, Ouchi K, Williams MV. The prevalence of inpatients at 33 U.S. hospitals appropriate for and receiving referral to palliative care. J Palliat Med. 2016;19(4):360–72.
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–51 Available from: http://linkinghub.elsevier.com/retrieve/pii/0895435694901295 [cited 19 Jun 2017].
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83 Available from: http://linkinghub.elsevier.com/retrieve/pii/0021968187901718 [cited 19 Jun 2017].
Van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–557. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20194559 [cited 20 Jun 2017].
Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk prediction models for hospital readmission: a systematic review NIH public access. JAMA. 2011;306(15):1688–98 Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3603349/pdf/nihms429222.pdf [cited 23 Aug 2019].
Brajer N, Cozzi B, Gao M, Nichols M, Revoir M, Balu S, et al. Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission. JAMA Netw Open. 2020;3(2):1–14.
Gama J, Fernandes R, Rocha R. Decision trees for mining data streams. Intell Data Anal. 2006;10(1):23–45.
Kotsiantis SB. Decision trees: a recent overview. Artif Intell Rev. 2013;39(4):261–83.
Wijaya A, Bisri A. Hybrid decision tree and logistic regression classifier for email spam detection. In: 2016 8th international conference on information technology and electrical engineering (ICITEE): IEEE; 2016. p. 1–4. Available from: http://ieeexplore.ieee.org/document/7863267/ [cited 5 Dec 2018].
Chen C, Li O, Tao D, Barnett A, Rudin C, & Su JK. This looks like that: deep learning for interpretable image recognition. Adv Neural Inf Process Syst. 2019;32:1–12.
Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1 Available from: https://www.jstor.org/stable/1269547?origin=crossref [cited 11 Jul 2018].
Gianfrancesco MA, Goldstein ND. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol. 2021;21(234). https://doi.org/10.1186/s12874-021-01416-5.
Mantel N, Hankey BF. A logistic Rugression analysis of response-time data where the Hazard function is time dependent. Commun Stat Theory Methods. 1978;7(4):333–47 Available from: http://www.tandfonline.com/doi/abs/10.1080/03610927808827627 [cited 14 May 2018].
Bertsimas D, Dunn J. Optimal classification trees. Mach Learn. 2017;106(7):1039–82.
Huang Y, Talwar A, Chatterjee S, & Aparasu RR. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol. 2021;21(1):1–14.
Artetxe A, Beristain A, Graña M. Predictive models for hospital readmission risk: a systematic review of methods. Comput Methods Prog Biomed. 2018;164:49–64.
Frome EL. The analysis of rates using Poisson regression models. Biometrics. 1983;39(3):665 Available from: http://www.jstor.org/stable/2531094?origin=crossref [cited 9 May 2018].
Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017;24(1):198–208 Available from: https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw042.
Dahlin-Ivanoff S, Gosman--Hedström G, Edberg A-K, Wilhelmson K, Eklund K, Duner A, et al. Elderly persons in the risk zone. Design of a multidimensional, health-promoting, randomised three-armed controlled trial for “prefrail” people of 80+ years living at home. BMC Geriatr. 2010;10(1):27 Available from: http://bmcgeriatr.biomedcentral.com/articles/10.1186/1471-2318-10-27.
Zhao H, Tanner S, Golden SH, Fisher SG, Rubin DJ. Common sampling and modeling approaches to analyzing readmission risk that ignores clustering produce misleading results. BMC Med Res Methodol. 2020;20(1):1–9 Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-020-01162-0 [cited 14 Jan 2022].