Combined unsupervised (clustering), supervised (classification) machine learning technique used to develop predictive model
THURSDAY, May 5, 2022 (HealthDay News) — A combined unsupervised and supervised machine learning (ML) technique can help predict long-term spinal cord stimulation (SCS) response for patients with chronic pain, according to a study published in the May issue of Neurosurgery.
Amir Hadanny, M.D., from Albany Medical College in New York, and colleagues developed ML-based predictive models of long-term SCS response. A combined unsupervised (clustering) and supervised (classification) ML technique, which included 31 features, was applied to data for a prospectively collected cohort of 151 patients.
The researchers identified two distinct clusters, and patients in the two clusters differed significantly in age, duration of chronic pain, preoperative numeric rating scale, and preoperative pain catastrophizing scale scores. The highest overall performance was demonstrated in logistic predictive models with a nested cross-validation using the 10 most influential features, with an area under the curve of 0.757 and 0.708 for the two clusters.
“Our study resulted in the development of a model to predict which patients would benefit from spinal cord stimulation,” a coauthor said in a statement. “After we validate this work, our hope is that this machine-learning model can inform a clinical decision support tool to help physicians better choose which patients may be most appropriate.”
Several authors disclosed financial ties to the biopharmaceutical and medical device industries.
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