Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome is a research paper published in PLoS ONE (2022). On theSindex it has a DataRank of 0. It has been cited 24 times.
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U.S. Department of Defense
Grant: SC150198
U.S. Department of Defense
Grant: SC190233
National Institute of Neurological Disorders and Stroke
Grant: R01NS088475
National Institute of Neurological Disorders and Stroke
Grant: UH3NS106899
U.S. Department of Veterans Affairs
Grant: 1I01RX002245
U.S. Department of Veterans Affairs
Grant: I01RX002787
National Institute of Neurological Disorders and Stroke
Grant: F32NS117728
RRD VA
Grant: I01 RX002245
NINDS NIH HHS
Grant: R01 NS122888
National Institutes of Health
Grant: 5UH3NS106899-05
Translational Outcomes Project: Visualizing Syndromic Information and Outcomes for Neurotrauma (TOP-VISION)
National Institutes of Health
Grant: 5F32NS117728-02
Leveraging Heterogeneity in Preclinical Traumatic Brain Injury to Drive Discovery and Reproducibility
National Institutes of Health
Grant: 5R01NS088475-05
Maladaptive Plasticity in Spinal Cord Injury: Cellular Mechanisms
National Institutes of Health
Grant: 5I01RX002245-08
Harnessing big-data for plasticity and rehabilitation in translational SCI
National Institutes of Health
Grant: 5I01RX002787-04
Leveraging data-science for discovery in chronic TBI
Craig H. Neilsen Foundation
Wings for Life
FWCI
0.99
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
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Sustainable Development Goals