Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Dec 28, 2022
Open Peer Review Period: Dec 28, 2022 - Feb 22, 2023
Date Accepted: Aug 9, 2023
(closed for review but you can still tweet)
Use of the RETAIN predictive model with nursing narratives to predict postoperative hospital stays: A retrospective cohort study
ABSTRACT
Background:
Nursing narratives comprise an intriguing feature in the prediction of short-term clinical outcomes. However, it is unclear which nursing narratives impact significantly the prediction of postoperative length of stay (LOS) in deep-learning models.
Objective:
Therefore, we applied the REverse Time AttentIoN (RETAIN) model to predict LOS, entering nursing narratives as the main input.
Methods:
A total of 354 patients who underwent ovarian cancer surgery at the Seoul National University Bundang Hospital during 2014–2020 were retrospectively enrolled. Nursing narratives collected within three postoperative days were used to predict prolonged LOS (≥ 10 days).
Results:
Nursing narratives entered on the first day were the most influential predictors in prolonged LOS. The likelihood of prolonged LOS increased if the physician had to check the patient often and if the patient received intravenous fluids or intravenous patient-controlled analgesia late.
Conclusions:
The use of RETAIN on nursing narratives predicted postoperative LOS effectively for patients who underwent ovarian cancer surgery. These findings suggest that accurate and interpretable deep-learning information obtained shortly after surgery may accurately predict prolonged LOS.
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