Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions

• Published in: Scientific reports Vol. 13; no. 1; p. 10479
• Main Authors: Tran, Nhat Quang, Goel, Gautam, Pudota, Nirmala, Suesserman, Michael, Helms, John, Lasaga, Daniel, Olson, Dan, Bowen, Edward, Bhattacharya, Sanmitra
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.06.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 639/705/117; 692/700/228; Calibration; Diagnosis; Embedding; Health care; Health Facilities; Health Personnel; Humanities and Social Sciences; Humans; multidisciplinary; Patient Readmission; Quality of Health Care; Science; Science (multidisciplinary); Survival; Survival analysis

Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers’ quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk...