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.

We provided an XACML-based implementation of a semantic-based privacy management framework that incorporates context into dynamic rule evaluation and decision enforcement. Our evaluation results are promising, and we believe that future enhancements on the current implementation can provide a foundation for modern health records infrastructures and inspire collaborative data sharing.

Some fairly recent research has focused on providing XACML-based solutions for dynamic privacy policy management. In this regard, a number of works have provided enhancements to the performance of XACML policy enforcement point (PEP) component, but very few have focused on enhancing the accuracy of that component. This paper improves the accuracy of an XACML PEP by filling some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide an XACML-based implementation of a dynamic privacy policy management framework and an evaluation of the applicability of our system in comparison to some of the existing approaches.