Deep Learning Model for Predicting High-Risk Transitions of Care Using Discharge Summaries, Medication Reconciliation Records, Follow-Up Appointment Status, Home Support Indicators, and Social Risk Variables

cirpublications.com-HMST-1790913210-j256166969 1790913210 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2024 4 2 Deep Learning Model for Predicting High-Risk Transitions of Care Using Discharge Summaries, Medication Reconciliation Records, Follow-Up Appointment Status, Home Support Indicators, and Social Risk Variables Alejandro Torres Miguel Fernandez 2024 10.68159/j256166969 https://cirpublications.com/pub/journal/2/article/j256166969 Ashfaq A, Sant’Anna A, Lingman M, Nowaczyk S. Readmission prediction using deep learning on electronic health records. J Biomed Inform. 2019;97:103256. Davis S, Zhang J, Lee I, Rezaei M, Greiner R, Padwal R. Effective hospital readmission prediction models using machine-learned features. BMC Health Serv Res. 2022;22(1):1415. Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018;18(1):44. Walsh CG, Sharman K, Hripcsak G. Beyond discrimination: a comparison of calibration methods and clinical usefulness of predictive models of readmission risk. J Biomed Inform. 2017;76:9-18. Jiang S, Chin KS, Qu G, Tsui KL. An integrated machine learning framework for hospital readmission prediction. Knowl Based Syst. 2018;146:73-90. Morgan DJ, Bame B, Zimand P, Dooley P, Thom KA, Harris AD. Assessment of machine learning vs standard prediction rules for predicting hospital readmissions. JAMA Netw Open. 2019;2(3):e190348. 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):96. Wang S, Zhu X. Predictive modeling of hospital readmission: challenges and solutions. IEEE/ACM Trans Comput Biol Bioinform. 2021;19(5):2975-95. Orangi-Fard N, Akhbardeh A, Sagreiya H. Predictive model for ICU readmission based on discharge summaries using machine learning and natural language processing. Informatics. 2022;9(1):10. Brown JR, Ricket IM, Reeves RM, Shah RU, Goodrich CA, Gobbel GT. Information extraction from electronic health records to predict readmission following acute myocardial infarction: does natural language processing using clinical notes improve prediction of readmission? J Am Heart Assoc. 2022;11(7):e024198. Huang K, Altosaar J, Ranganath R. ClinicalBERT: modeling clinical notes and predicting hospital readmission. arXiv [Preprint]. 2019:arXiv:1904.05342. Coppa K, Kim EJ, Oppenheim MI, Bock KR, Conigliaro J, Hirsch JS. Examination of post-discharge follow-up appointment status and 30-day readmission. J Gen Intern Med. 2021;36(5):1214-21. Navathe AS, Zhong F, Lei VJ, Chang FY, Sordo M, Topaz M, et al. Hospital readmission and social risk factors identified from physician notes. Health Serv Res. 2018;53(2):1110-36. Belouali A, Bai H, Raja K, Liu S, Ding X, Kharrazi H. Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction. JAMIA Open. 2022;5(2):ooac046. Zhang Y, Zhang Y, Sholle E, Abedian S, Sharko M, Turchioe MR. Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death. PLoS One. 2020;15(6):e0235064. Lax Y, Martinez M, Brown NM. Social determinants of health and hospital readmission. Pediatrics. 2017;140(5):e20171427. 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):18. Zhang D, Yin C, Zeng J, Yuan X, Zhang P. Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inform Decis Mak. 2020;20(1):280. Pham MK, Mai TT, Crane M, Ebiele M, Brennan R, Ward ME. Forecasting patient early readmission from Irish hospital discharge records using conventional machine learning models. Diagnostics (Basel). 2024;14(21):2405. Loutati R, Ben-Yehuda A, Rosenberg S, Rottenberg Y. Multimodal machine learning for prediction of 30-day readmission risk in elderly population. Am J Med. 2024;137(7):617-28. Moerschbacher A, He Z. Building prediction models for 30-day readmissions among ICU patients using both structured and unstructured data in electronic health records. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway (NJ): IEEE; 2023. p. 4368-73. Craig E, Arias C, Gillman D. Predicting readmission risk from doctors' notes. arXiv [Preprint]. 2017:arXiv:1711.10663. Michailidis P, Dimitriadou A, Papadimitriou T, Gogas P. Forecasting hospital readmissions with machine learning. Healthcare (Basel). 2022;10(6):981. Mahajan SM, Ghani R. Using ensemble machine learning methods for predicting risk of readmission for heart failure. In: Medinfo 2019. Lyon (France): IOS Press; 2019. p. 243-7. Chiewchantanakit D, Meakchai A, Pituchaturont N, Dilokthornsakul P, Dhippayom T. The effectiveness of medication reconciliation to prevent medication error: a systematic review and meta-analysis. Res Social Adm Pharm. 2020;16(7):886-94. Goto T, Jo T, Matsui H, Fushimi K, Hayashi H, Yasunaga H. Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease. COPD. 2019;16(5-6):338-43. Zumbrunn A, Bachmann N, Bayer-Oglesby L, Jörg R. Social disparities in unplanned 30-day readmission rates after hospital discharge in patients with chronic health conditions: a retrospective cohort study using patient level hospital administrative data linked to the population census in Switzerland. PLoS One. 2022;17(9):e0273342. Matty R, Heckmann R, George E, Barthel AB, Suter LG, Ross JS. Identification of hospitals that care for a high proportion of patients with social risk factors. JAMA Health Forum. 2021;2(7):e211323. Truong HP, Luke AA, Hammond G, Wadhera RK, Reidhead M, Maddox KE. Utilization of social determinants of health ICD-10 Z-codes among hospitalized patients in the United States, 2016-2017. Med Care. 2020;58(12):1037-43. Joynt Maddox KE, Reidhead M, Hu J, Kind AJH, Zaslavsky AM, Nagasako EM. Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program. Health Serv Res. 2019;54(2):327-36. Obuobi S, Chua RF, Besser SA, Tabit CE. Social determinants of health and hospital readmissions: can the HOSPITAL risk score be improved by the inclusion of social factors? BMC Health Serv Res. 2021;21(1):5. Yang MY, Kwak GH, Pollard TJ, Celi LA, Ghassemi M. Evaluating the impact of social determinants on health prediction in the intensive care unit. In: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. New York: ACM; 2023. p. 333-50. Zhao P, Yoo I, Naqvi SH. Early prediction of unplanned 30-day hospital readmission: model development and retrospective data analysis. JMIR Med Inform. 2021;9(3):e16306. Pishgar M, Theis J, Del Rios M, Ardati A, Anahideh H, Darabi H. Prediction of unplanned 30-day readmission for ICU patients with heart failure. BMC Med Inform Decis Mak. 2022;22(1):117.