A preliminary prediction model using a deep learning software program for prolonged hospitalization after
Ryota Murase1, Yasushige Shingu2, Satoru Wakasa1
1Department of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan.
Abstract:
A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction model of a prolonged LOS using a deep learning software program (Prediction One; Sony Network Communications Inc., Tokyo, Japan) using preoperative data. Subjects were 157 patients (121 for training data, 36 for validation data). A prolonged LOS was defined as a more than 30-day postoperative stay due to physical inactivity. The area under the receiver operating characteristic curve and the accuracy of the model in the validation data were 0.806 and 67%, respectively. In conclusion, the preliminary model demonstrated acceptable performance for the prediction of a prolonged LOS after cardiovascular surgery.
