AI is Redefining Alcoholism Screening in Firefighters

The pervasive nature of Artificial Intelligence (AI) is rapidly redefining risk assessment and Alcoholism Detection across critical medical and occupational fields. Where conventional screening relies heavily on subjective reports, often subject to bias or fear of career reprisal, AI tools offer objective, precise, and highly accurate prediction models for identifying vulnerability in High-Risk Groups.

One crucial area of application is in evaluating candidates for early liver transplantation (LT) due to alcohol-associated hepatitis (AH). Returning to harmful alcohol use (HAU) post-LT is the strongest predictor of death. Existing psychosocial scoring systems for AH patients often exhibit poor positive predictive value (PPV), ranging from 0% to 25%, thus limiting their clinical utility in deciding who receives a potentially life-saving organ.

Researchers employed an AI model (specifically XGBoost) trained on detailed psychosocial evaluation narratives from 10 transplant centers. This preliminary model demonstrated a superior positive predictive value of 82% in the external validation set, and an Area Under the Curve (AUC) of 0.930 in the training set, significantly outperforming conventional systems. The model revealed novel predictors related primarily to social support. Highly important variables included whether the patient’s primary support person for post-LT care had been identified, the presence of pediatric children/grandchildren living with the patient (hypothesized as a stressor), and if the patient was recently a home caregiver. Crucially, this AI model is intended as an adjunct tool to tailor interventions based on predicted risk, not to unilaterally deny transplant.

In the occupational sector, AI is tackling the cumulative mental stress faced by firefighters. Firefighters are a high-risk population for Alcohol Use Disorder (AUD) due to continuous trauma exposure and a culture that discourages displays of vulnerability, leading to under-reporting in self-screening. A multimodal deep learning framework was developed to screen this group objectively by integrating T1-weighted structural MRI neuroimaging data with standardized neuropsychological tests, such as the Grooved Pegboard Test. This "cooperative fusion" system classified firefighters at risk for AUD with approximately 80% accuracy. This accuracy represents a 17-percentage-point improvement over models using only clinical data or only neuroimaging data (which each achieved about 62% accuracy).

Further demonstrating the potential for objective Alcoholism Detection, a separate deep learning framework was proposed for the automated classification of alcoholic EEG signals. Utilizing LASSO regression for feature extraction and metaheuristic algorithms for refinement—with the Binary Dragonfly Algorithm (BDA) proving the most effective feature selection method—the system achieved an outstanding classification accuracy of 99.59% using an Enhanced Artificial Neural Network (EANN).

These results collectively signal a substantial shift: AI is enabling medical professionals to move past subjective screening limitations, offering personalized, highly precise risk stratification tools across diverse populations from transplant patients to trauma-exposed professionals.

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🔖 Sources

AI helps detect early signs of alcoholism in firefighters with 80% accuracy

Alcohol use in firefighters: A network model of behaviors and transdiagnostic risk

Artificial intelligence to identify harmful alcohol use after early liver transplant for alcohol‐associated hepatitis

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

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