116. Evaluation of the performance of machine learning models for the prediction of the operating room occupation time of non-elective surgeries
Invited abstract in session TA-1: Surgery scheduling 1, stream Sessions.
Tuesday, 9:00-10:30Room: NTNU, Realfagbygget R5
Authors (first author is the speaker)
| 1. | Anem Dupré
|
| CentraleSupelec | |
| 2. | Oualid Jouini
|
| Laboratoire Genie Industriel, Ecole Centrale Paris | |
| 3. | Guillaume Lamé
|
| Laboratoire de Génie Industriel, CentraleSupélec | |
| 4. | Thomas Botrel
|
| CHU Pitié-Salpêtrière, APHP |
Abstract
The optimal planning of non-elective surgeries is made difficult by a lack of visibility on patients’ future resource needs. Our study aims at improving the pre-operative visibility of schedulers through the prediction of the operating room occupation time (OT) and post-operative length of stay. We report initial results on OT prediction.
Four machine learning models (MLMs) were selected based on their performance on similar problems: Ridge Regression (RR), Random Forest (RF), XGBoost (XGB), and Multilayer Perceptron (MLP). Using nested cross-validation and a 20% validation set, the models were fitted on the data of patients that underwent non-elective surgery in a large French public teaching hospital between 2015 and 2018.
We included OT for 3,053 patients. On the validation set, the algorithms predicted durations within 20% of actual times (within 20 min for OTs below 100 min) for 58.6% of cases (95% CI [58.1, 590]) for RR, 59.2% [58.5, 59.9] for RF, 59.3% [58.8, 59.8] for XGB and 56.7% [55.8, 57.8] for MLP, respectively. In comparison, using the average of the previous 5 similar surgeries yielded a 48.6% [48.0, 49.4] performance. Surgeons’ predictions before surgery obtained 45.5% [44.3, 46.5] performance. Mean Absolute Percentage Error followed similar trends. Performance varied between surgical specialties.
MLMs improve predictions compared to surgeon’s estimations by around 10%. Further work is needed to better practical usefulness.
Keywords
- Emergency Medical Service
- Operating room planning and scheduling
- Performance evaluation
Status: accepted
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