TY  - JOUR
AU  - Han, Sungjoo
AU  - Kim, Yong Bum
AU  - No, Jae Hong
AU  - Suh, Dong Hoon
AU  - Kim, Kidong
AU  - Ahn, Soyeon
PY  - 2023
DA  - 2023/12/19
TI  - Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study
JO  - JMIR Med Inform
SP  - e45377
VL  - 11
KW  - discharge prediction
KW  - text mining
KW  - free text
KW  - extraction
KW  - length of stay
KW  - hospital stay
KW  - electronic health record
KW  - EHR
KW  - discharge
KW  - interpretable deep learning
KW  - risk prediction
KW  - nursing
KW  - machine learning
KW  - deep learning
KW  - predict
KW  - ovarian cancer
AB  - Background: Nursing narratives are an intriguing feature in the prediction of short-term clinical outcomes. However, it is unclear which nursing narratives significantly impact 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 from 2014 to 2020 were retrospectively enrolled. Nursing narratives collected within 3 postoperative days were used to predict prolonged LOS (≥10 days). The physician’s assessment was conducted based on a retrospective review of the physician’s note within the same period of the data model used. Results: The model performed better than the physician’s assessment (area under the receiver operating curve of 0.81 vs 0.58; P=.02). 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 the RETAIN model 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2023/1/e45377
UR  - https://doi.org/10.2196/45377
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38131977
DO  - 10.2196/45377
ID  - info:doi/10.2196/45377
ER  -