Improving Patients’ Length of Stay Prediction Using Clinical and Demographics Features Enrichment

Predicting patients’ length of stay (LOS) is crucial for efficient scheduling of treatment and strategic future planning, in turn reduce hospitalisation costs. However, this is a complex problem requiring careful selection of optimal set of essential factors that significantly impact the accuracy and performance of LOS prediction. Using an inpatient dataset of 285k of records from 14 general care hospitals in Vermont, USA from 2013–2017, we presented our novel approach to incorporate features to improve the accuracy of LOS prediction. Our empirical experiment and analysis showed considerable improvement in LOS prediction with an XGBoost model RMSE score of 6.98 and R2 score of 38.24%. Based on several experiments, we provided empirical analysis of the importance of different feature sets and its impact on predicting patients’ LOS.