MASTER Assignment
30-days post-operative mortality prediction of elderly hip fracture patients
Type : Master M-CS
Period: Nov, 2019- July, 2020
Student : Yenidoğan, B. (Berk, Student M-CS)
Date Final project: July, 8, 2020
Supervisors:
Abstract:
Hip fractures on the elderly are a major health care problem in society. In the clinic, it is important to identify high-risk patients to guide the decision making with respect to the treatment of the patient. This study presents a prediction model for 30-days mortality of elderly hip fracture patients by following a multimodal machine learning approach. This approach fuses the image modality with the structured modality for the prediction task. At the same time, it also addresses the problems related to the class imbalanced dataset and the high number of missing values. The early fusion model, developed in this study, first extracts features from the chest and hip x-ray images by the use of convolutional neural networks. Subsequently, it combines extracted features with structured modality and feeds into a Random Forest Classifier to finalize the prediction. The proposed model outperforms a replicated version of Almelo Hip Fracture Score (AHFS-a) with an AUC score of 0.742 vs 0.706. Finally, by the analysis of feature importances, this study also demonstrates that chest x-ray images contain important signs related to 30-days mortality of elderly hip fracture patients.