Does a domain-aware interpretable machine learning model accurately predict postoperative hospital length of stay from perioperative data?
An interpretable machine learning model using perioperative data can estimate postoperative length of stay, potentially improving hospital resource allocation and perioperative planning.
Interpretable machine learning enables accurate and generalizable estimation of postoperative LOS while revealing clinically actionable perioperative domains. Such frameworks may facilitate more efficient perioperative planning, improved allocation of hospital resources, and personalized recovery strategies.
Hussain et al. (Tue,) studied this question.