Physicians in Oklahoma often aim to diagnose and treat traumatic brain injury victims in a way that minimizes the risk of secondary issues. However, it's not always easy to achieve this goal. This is why the National Science Foundation (NSF) is funding a four-year study that will involve the use of new machine learning techniques to produce models that may be able to more accurately categorize patients, predict short- and long-term outcomes for TBI victims and present patient-specific intervention recommendations.
Affecting more than 10 million people worldwide, traumatic brain injury has the potential to become worse if secondary injuries aren't detected, treated or prevented. Researchers plan to use unique computational algorithms to provide new insights and tools for physicians so they can be more proactive with their interventions.
Researchers and clinical experts who will be part of the study plan to use a combination of inpatient bedside data and remotely gathered data to develop a better understanding of TBI patient populations. It's also believed that tools developed from the study could help clinicians and physicians select more appropriate patients for brain injury-related clinical trials. Researchers further hope the NSF-funded study may be able to allow TBI patients or their family members to make long-term care decisions based on more specific information. There's also the possibility of reducing health care and societal costs associated with TBIs with new tools and techniques that might come from the study.
If negligence is a suspected contributing factor to a traumatic brain injury, a lawyer could put together a personal injury case. Responsible parties might include drivers, attackers or medical personnel who may have made serious mistakes during initial treatment efforts. Because of the potential long-term impact of brain injuries, appropriate compensation is often used to ease the financial burden of rehabilitation.