Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine

Regulatory and Ethical Considerations on Artificial Intelligence for Occupational Medicine

Generative artificial intelligence and Large Language Models are reshaping labor dynamics and occupational health practices. As AI continues to evolve, there's a critical need to customize ethical considerations for its specific impacts on occupational health. Recognizing potential ethical challenges and dilemmas, stakeholders and physicians are urged to proactively adjust the practice of occupational medicine in response to shifting ethical paradigms. By advocating for a comprehensive review of the International Commission on Occupational Health ICOH code of Ethics, we can ensure responsible medical AI deployment, safeguarding the well-being of workers amidst the transformative effects of automation in healthcare.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.

Results

With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.

Discussion

Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

Ayurvedic clinical decision-making methods to predict, prevent and manage childhood allergic disorders

Allergy is a conundrum lacking satisfactory answers despite its global prevalence. Traditional systems of medicine may contain sustainable and effective solutions for the same. For mainstreaming them, an evaluation based on the system's own methods is inevitable rather than symptom-based correlations. Atopy is a novel entity in Ayurveda, but the methods of tripartite delineation (disease pattern, disease targets and influencing factors) of novel diseases and multifactorial approach to diagnosis and management in Ayurveda can bring about comprehensiveness in collection and categorization of data regarding the entity. This in turn can make the prediction, prevention and management of the same more precise, effective and sustainable. The article provides a template for the application of Ayurvedic biological framework in the diagnosis and management of novel diseases, with special reference to childhood allergic disorders.

[Prospects for personalized Helicobacter pylori eradication therapy. Pro et contra]

The article is devoted to debatable aspects of Helicobacter pylori eradication therapy. The eradication cancer-preventive effects are considered. A negative comorbidity between H. pylori infection and such diseases as gastroesophageal reflux disease, inflammatory bowel disease and bronchial asthma has been described. The perspectives of populational, individualized and personalized strategies for H. pylori eradication therapy assessment are given. Recommendations for individualized H. pylori eradication strategy implementation in practical healthcare are proposed.