The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae. Exp Neurol. 2020 Nov 03;:113526 Authors: Schultebraucks K, Chang BP Abstract Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we a...
Source: Experimental Neurology - Category: Neurology Authors: Tags: Exp Neurol Source Type: research