The Veterans Disability and Rehabilitation Research Channel is delighted to welcome Noam Harel as a Channel Editor!
The Veterans Disability and Rehabilitation Research Channel features articles on a wide range of topics relevant to veteran disability and rehabilitation research. The Channel Editors aim to showcase the most up to date research to assist veterans and all adults around the world with chronic illness and disability. Joining Channel Editors Lisa Brenner, Mary Elizabeth Bowen and Yih-Kuen Jan, Noam will bring his expertise in neurology with an emphasis in spinal cord injury (SCI) and amyotrophic lateral sclerosis (ALS).
Meet Our New Channel Editor
I am delighted to join the PLOS Veterans Disability & Rehabilitation Research Channel. The public, and our Veterans, should benefit from full and open dissemination of all research knowledge.
I am a neurologist with a background in molecular biology. I received my MD and PhD from the University of Pennsylvania, then completed a residency in neurology at Columbia University. I am now an investigator at the Center of Excellence for the Medical Consequences of Spinal Cord Injury at the James J. Peters VA Medical Center in Bronx, NY, and I am an Associate Professor in Neurology and Rehabilitation Medicine at Icahn School of Medicine at Mount Sinai. I am on the Editorial Board for Neurology, and on the Board of Directors for the American Society of Neurorehabilitation.
My team’s research focuses on the use of magnetic and electrical stimulation of the nervous system in an attempt to improve nerve function after SCI and ALS. For reasons that are still unknown, ALS is more common in Veterans.
Channel Update Highlight: Editor’s Pick
In our most recent Channel update, Noam’s “Editor’s Pick” highlights recent work from Dr Chenxi Huang (Yale-New Haven Hospital) and colleagues entitled “Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study” published in PLOS Medicine.
The paper is a prime example of how applying machine-learning algorithms to ‘big data’ isn’t just for search engines and social networking apps anymore. In the realm of analyzing baseline data to predict clinical outcomes, machine-learning algorithms require fewer subjective assumptions and less conversion of raw numbers into categorical variables than traditional approaches to risk stratification. The authors applied 6 machine-learning models to existing data from a national registry on acute kidney injury (AKI) after cardiac catheterization. They found that the best machine-learning model predicted AKI more accurately than the prior regression-based model. Use of machine-learning algorithms for better risk prediction could improve care in a variety of clinical contexts.
Find more content like this and keep up to date on the latest veteran disability & rehabilitation research by following the Veterans Disability & Rehabilitation Research Channel. You can also send any questions or content recommendations to email@example.com or tweet us @PLOSChannels using the hashtag #PLOSVeterans. The PLOS Channels Team would also like to thank Thomas E Stripling for his tenure as Channel Editor.
The Veterans Disability and Rehabilitation Research Channel was developed with the U.S. Department of Veterans Affairs Rehabilitation Research and Development Service as a new home for the community previously served by the Journal of Rehabilitation Research & Development (JRRD).
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