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Military Personnel

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User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, w...

A Novel Application of Head Tracking Data in the Analysis and Assessment of Operational Cervical Spine Range of Motion for Army Aviators.

Military medicine
INTRODUCTION: Neck pain among rotary-wing aviators has been established as an important issue in the military community, yet no U.S. Army regulation defines exactly what cervical spine range of motion (CROM) is adequate for flight. This lack of regul...

Rhabdomyolysis in a Civil Aviation Pilot.

Aerospace medicine and human performance
Rhabdomyolysis is a potentially fatal disease caused by trauma, infections, and toxins. Rhabdomyolysis has not been reported in Chinese civil aircrew, but in our case report a male civil copilot contracted rhabdomyolysis after excessive exercise, sh...

Machine learning prediction of combat basic training injury from 3D body shape images.

PloS one
INTRODUCTION: Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic...

The implications of emerging technology on military human performance research priorities.

Journal of science and medicine in sport
OBJECTIVES: To demonstrate the need for the military human performance research community to anticipate and evolve with the emergence of new and disruptive battlefield technologies that are changing the fundamental role of the human combatant.

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Psychiatry research
Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the low base rate and underpowered statistical approaches. The objective of the current study was to use...

Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.

Military medicine
INTRODUCTION: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develo...

Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.

Archives of pathology & laboratory medicine
CONTEXT.—: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI.

Future of service member monitoring: the intersection of biology, wearables and artificial intelligence.

BMJ military health
While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed...