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

作者：Lee Sunghoon Ivan^*; Mortazavi Bobak^*; Hoffman Haydn A^*; Lu Derek S^*; Li Charles; Paak Brian H^*; Garst Jordan H^*; Razaghy Mehrdad^*; Espinal Marie^*; Park Eunjeong^*; Lu Daniel C^*; Sarrafzadeh Majid^*

来源：IEEE Journal of Biomedical and Health Informatics, 2016, 20(1): 91-99.

DOI：10.1109/JBHI.2014.2372777

• Functional outcomes
• gaussian process regression (GPR)
• handgrip
• prediction
• spinal cord disorder
• target tracking
• truncated normal distribution

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.

• 出版日期2016-1