From Engel's Bio-Psycho-Social model to the personalized health determinants model: a comprehensive framework and...

Engel's Bio-Psycho-Social (BPS) model (1977) reframed healthcare by integrating biological, psychological, and social perspectives. Despite its influence, the model has been criticized for insufficient specificity in domains critical to precision health, including nutrition, lifestyle, socioeconomic, environmental, and structural factors. To address these limitations, we propose the Personalized Health Determinants Model (PHDm), a comprehensive nine-dimension framework, Biological, Psychological, Social, Cultural, Environmental, Economic, Political, Spiritual, and Lifestyle, synthesized from Engel's BPS model, the WHO Commission on Social Determinants of Health, CDC frameworks, and related literature. Section 2.0 provides a detailed evidence-based rationale for the dimensions and their relationship to prior frameworks. The PHDm organizes each dimension hierarchically into categories, sub-categories, and measurable health elements (theoretical maximum -72,000), with only tailored, clinically relevant subsets applied in practice. Using obesity as a case study, we illustrate operationalization through a Bayesian Network and a complementary rule-based scoring system. Four exemplar factors, insulin sensitivity, dietary fiber, caloric intake, and activity frequency, are mapped into both models, enabling individualized obesity risk quantification and intervention simulation. Preliminary evaluation on NHANES data yielded -85% predictive accuracy for the Bayesian Network and -80% concordance for the rule-based system. This manuscript presents the PHDm as a conceptual framework with illustrative operationalization. While the Bayesian Network and rule-based approaches demonstrate promising preliminary performance on NHANES data, the current model, based on four simplified factors and binary thresholds, remains a proof-of-concept prototype and is not yet a clinically validated decision-support tool. Further development, external validation, and integration into electronic health records will be required before clinical deployment.