A machine learning-based mixture density network predicted surgical case duration with a continuous ranked probability score of 18.1 minutes, outperforming a Bayesian statistical method (21.2 minutes).
Observational (n=52,735)
No
Does a mixture density network machine learning model improve the prediction accuracy of surgical case durations compared to traditional statistical and tree-based methods in pediatric surgical cases?
A machine learning approach using a mixture density network and natural language processing of surgical descriptors provides more accurate, probabilistic forecasts of surgical case durations than traditional methods.
Absolute Event Rate: 18.1% vs 21.2%
OBJECTIVE: Accurate estimations of surgical case durations can lead to the cost-effective utilization of operating rooms. We developed a novel machine learning approach, using both structured and unstructured features as input, to predict a continuous probability distribution of surgical case durations. MATERIALS AND METHODS: The data set consisted of 53 783 surgical cases performed over 4 years at a tertiary-care pediatric hospital. Features extracted included categorical (American Society of Anesthesiologists ASA Physical Status, inpatient status, day of week), continuous (scheduled surgery duration, patient age), and unstructured text (procedure name, surgical diagnosis) variables. A mixture density network (MDN) was trained and compared to multiple tree-based methods and a Bayesian statistical method. A continuous ranked probability score (CRPS), a generalized extension of mean absolute error, was the primary performance measure. Pinball loss (PL) was calculated to assess accuracy at specific quantiles. Performance measures were additionally evaluated on common and rare surgical procedures. Permutation feature importance was measured for the best performing model. RESULTS: MDN had the best performance, with a CRPS of 18.1 minutes, compared to tree-based methods (19.5-22.1 minutes) and the Bayesian method (21.2 minutes). MDN had the best PL at all quantiles, and the best CRPS and PL for both common and rare procedures. Scheduled duration and procedure name were the most important features in the MDN. CONCLUSIONS: Using natural language processing of surgical descriptors, we demonstrated the use of ML approaches to predict the continuous probability distribution of surgical case durations. The more discerning forecast of the ML-based MDN approach affords opportunities for guiding intelligent schedule design and day-of-surgery operational decisions.
Jiao et al. (Fri,) conducted a observational in Surgical cases (n=52,735). Mixture density network (MDN) machine learning model vs. Tree-based methods and a Bayesian statistical method was evaluated on Continuous ranked probability score (CRPS) for predicting surgical case duration. A machine learning-based mixture density network predicted surgical case duration with a continuous ranked probability score of 18.1 minutes, outperforming a Bayesian statistical method (21.2 minutes).