Posts tagged prediction model
Articles and news from the latest research reports.
A new model for predicting recovery after spinal cord injury
The novel prediction model, which combines acute functional measures and evidence of injury on magnetic resonance imaging (MRI) including swelling and bleeding around the spinal cord, and which was drawn from two large clinical datasets, could help guide treatment decisions, classification of patents for clinical trials, and counseling of patients and families.
Jefferson Wilson, MD, Michael Fehlings, MD, PhD, from University of Toronto and Toronto Western Hospital, Canada, and colleagues from the U.S. describe the prediction model and its potential applications in the article “A Clinical Prediction Model for Long-Term Functional Outcome after Traumatic Spinal Cord Injury Based on Acute Clinical and Imaging Factors.”
"An important goal of medical research is to identify early surrogate markers that could assist treating physicians in determining appropriate therapeutic strategies," says W. Dalton Dietrich, III, PhD, The Miami Project to Cure Paralysis, University of Miami, FL, and Deputy Editor of the Journal. "This article provides important information that could help predict the potential for recovery after SCI and thereby direct treatment options."
Using Data to Predict Your Future Health
Have you ever gone on a trip and unexpectedly found yourself in need of medical care? What if your condition could have been predicted? Better yet, what if you already had the medicine needed to treat that condition in your luggage?
The Hierarchical Association Rule Model (HARM), which I co-developed with Tyler McCormick of the University of Washington and David Madigan of Columbia University, can help patients be better prepared by warning them (and their doctors) about the conditions they may likely experience next. The predictive modeling tool checks data about an individual patient against other patients in the database with similar situations to help determine future conditions. It also alerts patients about any higher risks they may have for certain types of conditions.