Author:
Sheetrit Eitam,Brief Menachem,Elisha Oren
Abstract
AbstractA hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a significant problem in the healthcare domain, as they lead to increased hospitalization costs, decreased patient satisfaction, and increased risk of adverse outcomes such as infections, medication errors, and even death. The problem of hospital readmissions is particularly acute in intensive care units (ICUs), due to the severity of the patients’ conditions, and the substantial risk of complications. Predicting Unplanned Readmissions in ICUs is a challenging task, as it involves analyzing different data modalities, such as static data, unstructured free text, sequences of diagnoses and procedures, and multivariate time-series. Here, we investigate the effectiveness of each data modality separately, then alongside with others, using state-of-the-art machine learning approaches in time-series analysis and natural language processing. Using our evaluation process, we are able to determine the contribution of each data modality, and for the first time in the context of readmission, establish a hierarchy of their predictive value. Additionally, we demonstrate the impact of Temporal Abstractions in enhancing the performance of time-series approaches to readmission prediction. Due to conflicting definitions in the literature, we also provide a clear definition of the term Unplanned Readmission to enhance reproducibility and consistency of future research and to prevent any potential misunderstandings that could result from diverse interpretations of the term. Our experimental results on a large benchmark clinical data set show that Discharge Notes written by physicians, have better capabilities for readmission prediction than all other modalities.
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Centers for Disease Control and Prevention (CDC). Hospital Utilization (in non-Federal short-stay hospitals). https://www.cdc.gov/nchs/fastats/hospital.htm (2018).
2. Lin, Y. W., Zhou, Y., Faghri, F., Shaw, M. J. & Campbell, R. H. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PloS One 14(7), e0218942 (2019).
3. Jencks, S. F., Williams, M. V. & Coleman, E. A. Rehospitalizations among patients in the Medicare fee-for-service program. New Engl. J. Med. 360(14), 1418–1428 (2009).
4. Rau, J. 10 Years of hospital readmissions penalties. https://www.kff.org/health-reform/slide/10-years-of-hospital-readmissions-penalties/ (2021).
5. 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. 22(1), 1–12 (2022).
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