A Prediction Model for Functional Outcomes in Spinal Cord Disorder Patients Using Gaussian Process Regression

Abstract—Predicting the functional outcomes of spinal cord disorder patients after medical treatments, such as a surgical operation, has always been of great interest. Accurate posttreatment prediction is especially beneficial for clinicians, patients, care givers, and therapists. This paper introduces a prediction method for postoperative functional outcomes by a novel use of Gaussian process regression. The proposed method specifically considers the restricted value range of the target variables by modeling the Gaussian process based on a truncated Normal distribution, which significantly improves the prediction results. The prediction has been made in assistance with target tracking examinations using a highly portable and inexpensive handgrip device, which greatly contributes to the prediction performance. The proposed method has been validated through a dataset collected from a clinical cohort pilot involving 15 patients with cervical spinal cord disorder. The results show th...

• Type: Journal
• Year: 2016
• Where: TITB
• Authors: Sunghoon Ivan Lee, Bobak Mortazavi, Haydn A. Hoffman, Derek S. Lu, Charles Li, Brian H. Paak, Jordan H. Garst, Mehrdad Razaghy, Marie Espinal, Eunjeong Park, Daniel C. Lu, Majid Sarrafzadeh
• Keywords: Education, TITB 2016