cirpublications.com-lvBh-1790883433-e263842856 1790883433 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 Anna Kowalska Piotr Nowak Tomasz Zielinski Katarzyna Mazur 2024 10.68159/e263842856 https://cirpublications.com/pub/journal/2/article/e263842856 Wang Y, Iyengar V, Hu J, Kho D, Falconer E, et al. Predicting future high-cost schizophrenia patients using high-dimensional administrative data. Front Psychiatry. 2017;8:114. Yang C, Delcher C, Shenkman E, Ranka S. Machine learning approaches for predicting high cost high need patient expenditures in health care. Biomed Eng Online. 2018;17(Suppl 1):131. Osawa I, Goto T, Yamamoto Y, Tsugawa Y. Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data. NPJ Digit Med. 2020;3(1):148. Vimont A, Leleu H, Durand-Zaleski I. Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France. Eur J Health Econ. 2022;23(2):211-23. de Ruijter UW, Kaplan ZR, Bramer WM, Eijkenaar F, Nieboer D, et al. Prediction models for future high-need high-cost healthcare use: a systematic review. J Gen Intern Med. 2022;37(7):1763-70. Langenberger B, Schulte T, Groene O. The application of machine learning to predict high-cost patients: a performance-comparison of different models using healthcare claims data. PLoS One. 2023;18(1):e0279540. Howson SN, McShea MJ, Ramachandran R, Burkom HS, Chang HY, et al. Improving the prediction of persistent high health care utilizers: retrospective analysis using ensemble methodology. JMIR Med Inform. 2022;10(3):e33212. Choi Y, An J, Ryu S, Kim J. Development and evaluation of machine learning-based high-cost prediction model using health check-up data by the national health insurance service of Korea. Int J Environ Res Public Health. 2022;19(20):13672. Wu Z, Berkowitz SA, Heagerty PJ, Benkeser D. A two-stage super learner for healthcare expenditures. Health Serv Outcomes Res Methodol. 2022;22(4):435-53. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18. Li Y, Rao S, Solares JR, Hassaine A, Ramakrishnan R, et al. BEHRT: transformer for electronic health records. Sci Rep. 2020;10(1):7155. Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4(1):86. Morid MA, Sheng OR, Kawamoto K, Ault T, Dorius J, et al. Healthcare cost prediction: leveraging fine-grain temporal patterns. J Biomed Inform. 2019;91:103113. Morid MA, Sheng OR, Kawamoto K, Abdelrahman S. Learning hidden patterns from patient multivariate time series data using convolutional neural networks: a case study of healthcare cost prediction. J Biomed Inform. 2020;111:103565. Lequertier V, Wang T, Fondrevelle J, Augusto V, Duclos A. Hospital length of stay prediction methods: a systematic review. Med Care. 2021;59(10):929-38. Herrero-Zazo M, Fitzgerald T, Taylor V, Street H, Chaudhry AN, et al. Using machine learning to model older adult inpatient trajectories from electronic health records data. iScience. 2023;26(1):105987. Jödicke AM, Zellweger U, Tomka IT, Neuer T, Curkovic I, et al. Prediction of health care expenditure increase: how does pharmacotherapy contribute? BMC Health Serv Res. 2019;19(1):953. Panay B, Baloian N, Pino JA, Peñafiel S, Sanson H, et al. Feature selection for health care costs prediction using weighted evidential regression. Sensors (Basel). 2020;20(16):4392. Wickramasinghe N. A convolutional net for medical records. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway (NJ): IEEE; 2019. p. 4563-6. Choi E, Bahadori MT, Song L, Stewart WF, Sun J. GRAM: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM; 2017. p. 787-95. Ma F, Chitta R, Zhou J, You Q, Sun T, Gao J. Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM; 2017. p. 1903-11. Luo L, Li J, Lian S, Zeng X, Sun L, Li C, et al. Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China. Health Informatics J. 2020;26(3):1577-98. 10.1177/1460458219881335 Panay B, Baloian N, Pino JA, Peñafiel S, Sanson H, Bersano-Méndez NI. Predicting health care costs using evidence regression. Proceedings. 2019;31(1):74. Chrusciel J, Girardon F, Roquette L, Laplanche D, Duclos A, Sanchez S. The prediction of hospital length of stay using unstructured data. BMC Med Inform Decis Mak. 2021;21(1):351. 10.1186/s12911-021-01722-4 Jain R, Singh M, Rao AR, Garg R. Predicting hospital length of stay using machine learning on a large open health dataset. BMC Health Serv Res. 2024;24(1):860. Drewe-Boss P, Enders D, Walker J, Ohler U. Deep learning for prediction of population health costs. BMC Med Inform Decis Mak. 2022;22(1):32. Gopukumar D, Ghoshal A, Zhao H. Predicting readmission charges billed by hospitals: machine learning approach. JMIR Med Inform. 2022;10(8):e37578. Yang P, Qiu H, Wang L, Zhou L. Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning. Expert Syst Appl. 2022;210:118541. Orji U, Ukwandu E. Machine learning for an explainable cost prediction of medical insurance. Mach Learn Appl. 2024;15:100516. Rao AR, Jain R, Singh M, Garg R. Predictive interpretable analytics models for forecasting healthcare costs using open healthcare data. Healthc Anal. 2024;6:100351. Wu C, Wu F, Huang Y, Xie X. NICE: neural in-hospital cost estimation from medical records. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM; 2019. p. 2409-12. Tan JK, Quan L, Salim NN, Tan JH, Goh SY, Thumboo J, et al. Machine learning–based prediction for high health care utilizers by using a multi-institutional diabetes registry: model training and evaluation. JMIR AI. 2024;3(1):e58463. Fan S, Abulizi A, You Y, Huang C, Yimit Y, Li Q, et al. Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning. BMC Infect Dis. 2024;24(1):875. 10.1186/s12879-024-09771-6