Title
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 that the proposed method can accurately predict postoperative functional outcomes, Oswestry disability index and target tracking scores, based on the patient's preoperative information with a mean absolute error of 0.079 and 0.014 (out of 1.0), respectively.
Year
DOI
Venue
2016
10.1109/JBHI.2014.2372777
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Keywords
Field
DocType
Functional outcomes,gaussian process regression (GPR),handgrip,prediction,spinal cord disorder,target tracking,truncated normal distribution
Kriging,Truncated normal distribution,Informatics,Spinal Cord Disorder,Physical therapy,Mean absolute error,Oswestry Disability Index,Gaussian process,Physical medicine and rehabilitation,Cohort,Medicine
Journal
Volume
Issue
ISSN
20
1
2168-2194
Citations 
PageRank 
References 
1
0.43
4
Authors
12