Artikel ; Online: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong.
BMC medical research methodology
2023 Band 23, Heft 1, Seite(n) 14
Abstract: Background: Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model ... ...
| Abstract | Background: Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants. Methods: A temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution's expected count. tZIP was externally validated with a retrospective cohort's rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model's prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator's marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses. Results: The tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator's contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status. Conclusions: A novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients' rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time. |
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| Mesh-Begriff(e) | Aged ; Humans ; Patient Readmission ; Retrospective Studies ; Hong Kong ; Hospitalization ; Machine Learning |
| Sprache | Englisch |
| Erscheinungsdatum | 2023-01-13 |
| Erscheinungsland | England |
| Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2041362-2 |
| ISSN | 1471-2288 ; 1471-2288 |
| ISSN (online) | 1471-2288 |
| ISSN | 1471-2288 |
| DOI | 10.1186/s12874-022-01824-1 |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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