Quality of life analysis in community pharmacy using deep learning and explainability methods

Objectives The study aimed to develop a deep learning-based model, using global and local explainability methods, to process clinical data collected in community pharmacies and identify the key variables influence health-related quality of life in patients with chronic diseases. Materials and Methods Data from 347 chronic patients, including 257 variables, were analyzed. Five predictive models were compared using 10-way stratified cross-validation: Gradient Boosting, Random Forest, LightGBM, a fully connected neural network (FCNN), and a set of 5 FCNNs. For interpretability, SHapley Additive exPlanations (SHAP) was used for the global importance of variables and Local Interpretable Model-Agnostic Explanations (LIME) for the local interpretation of individual cases. Results The FCNN ensemble achieved the best performance (R2 = 0.511 ± 0.126; 95% CI: 0.385-0.637; Mean Absolute Error = 0.0819 ± 0.0088; Mean Squared Error = 0.0122 ± 0.0039). Tree-based models showed slightly lower performance (eg, Gradient Boosting R2 = 0.484 ± 0.113). Explainability analysis identified pain, mobility limitations, beta-blocker use, anxiety/depression symptoms, and difficulties with activities of daily living as the most influential variables. Discussion The findings highlight that deep learning models can capture complex relationships among multiple clinical and psychosocial variables. The combination of SHAP and LIME allows for clinically interpretable results, facilitating personalized decisions in chronic disease care. Furthermore, the accessibility of community pharmacies provides a practical setting for data collection and application of these predictive tools. Conclusions The study demonstrates the potential of machine learning to support personalized decision-making in the management of chronic diseases from accessible settings such as community pharmacies, identifying the most important factors affecting patients’ quality of life.