Prediction of Postoperative Hospital Stay with Deep Learning Based on 101 654 Operative Reports in Neurosurgery

Electronic Health Records (EHRs) conceal a hidden knowledge that could be mined with data science tools. This is relevant for N.N. Burdenko Neurosurgery Center taking the advantage of a large EHRs archive collected for a period between 2000 and 2017. This study was aimed at testing the informativeness of neurosurgical operative reports for predicting the duration of postoperative stay in a hospital using deep learning techniques. The recurrent neuronal networks (GRU) were applied to the word-embedded texts in our experiments. The mean absolute error of prediction in 90% of cases was 2.8 days. These results demonstrate the potential utility of narrative medical texts as a substrate for decision support technologies in neurosurgery.