This study introduces an innovative explainable AI (XAI) pipeline designed to predict breast cancer survival by integrating clinical, socioeconomic, and geographic data. Using data from 10,172 patients treated at hospitals in the Memphis, Tennessee metropolitan area, the pipeline identifies key survival determinants and reveals significant survival disparities affecting Black women. Advanced machine learning models combined with SHapley Additive exPlanations (SHAP) provide actionable and interpretable insights into the role of tumor stage, socioeconomic conditions, and access to preventive care. This framework facilitates personalized survival predictions and targeted equity-focused interventions, demonstrating the potential of multi-source data integration to address health inequities and improve patient outcomes.

This study reports on the progress in designing and developing a framework for integrating heterogeneous datasets—structured, semi-structured, and unstructured—into an AI-ready multimodal data pipeline aimed at predicting radiation therapy interruptions (RTI) and enhancing patient care navigation. The AI-Ready dataset incorporates a broad set of information, including patient demographics, health data, clinical notes, medical imaging, and data on social determinants of health. Preliminary results indicate that this pipeline effectively integrates diverse distributed data sources, providing a foundation for training AI models capable of generating reliable and actionable predictions.

Adverse Childhood Experiences (ACEs) are a public health crisis. The American Academy of Pediatrics (AAP) recommends routine screening for ACEs. Current challenges in practice include a lack of validated screening tools, lack of resources to address issues found on screening, and the inability to translate population outcomes to individual patient care. Health care providers, and researchers are seeking innovative approaches and tools for ACEs screening, diagnosis, management, and continuous monitoring. Multimodal AI is an emerging concept that combines different input modalities to train AI agents to learn more accurate results by using both content and context. We present the Semantic Platform for Adverse Childhood Experiences Surveillance (SPACES), an explainable multimodal AI platform to facilitate ACEs surveillance and diagnosis of related health conditions, and subsequent interventions. We utilize a bottom-up approach to multimodal, explainable knowledge graph-based learning to derive recommendations and insights for better resource allocation and care management. SPACEs provides a novel approach to active ACEs surveillance by utilizing 360-degree views about patients and populations.

The coronavirus disease 2019 (COVID-19) is an infectious disease with high transmissibility and acquired through the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Scientists, physicians, and health officials are seeking innovative approaches to understand the complex COVID-19 pandemic pathway and decrease its morbidity and mortality. Incorporating artificial intelligence and data science techniques across the health science domain could improve disease surveillance, intervention planning, and policymaking. In this paper, we report our effort on the deployment of multimodal big data analytics to improve pandemic surveillance and preparedness. A common challenge for conducting multimodal big data analytics in clinical and public health settings is the issue of the integration of multidimensional heterogeneous data sources. Additional challenges for developers are explaining decisions and actions made by intelligent systems to human users, maintaining interpretability between different data sources, and privacy of health information. We present Urban Population Health Observatory (UPHO), an explainable knowledge-based multimodal data analytics platform to facilitate CoVID-19 surveillance by integrating a large volume of multimodal multidimensional, heterogenous data including social determinants of health indicators, clinical and population health data.