Gynecological Surgery and Machine Learning: Complications and Length of Stay Prediction

In this study we are developing predictive models for a length of stay after a gynecological surgery, complications and the length of the surgery using machine learning methods. The study was performed with the data of patients with the diseases of the female reproductive system. The patients were admitted to the Almazov National Medical Research Centre (Saint-Petersburg, Russia) within the period 2010-2020. The study included 8170 electronic medical records of inpatient episodes including 3500 operation protocols. The data included anamnesis of life, anamnesis of disease, laboratory tests, severity, outcome of a surgery, main and comorbid diagnosis, complications, case outcome. The dataset was randomly split into 70% train and 30% test datasets. Validation with the test dataset provided the following prediction metrics for the length of stay after a surgery model. Training score: AUC of ROC: 0.9582230976834093; K-fold CV average score: -8.73; MSE: 5.65; RMSE: 2.83.