My research interests are: Artificial Intelligence and Health Informatics/Digital Health. The goal of my interdisciplinary research program is to investigate Artificial Intelligence (AI) methods to impact healthcare practices and to innovate healthcare solutions at the public, patient, practitioner and health system levels. My research involves a synergistic interplay between data-driven and knowledge-driven AI.

I am particularly interested in data-driven AI to derive insights from complex health data (such as clinical, omics, operational, wearables, public health data) to answer questions such as: how to improve clinical decision making? how to predict trends and outcomes? how to optimize system use? and how to improve health outcomes? In this regard, I investigate and develop machine learning, data fusion and data visualization methods to annotate, integrate and analyze healthcare data to develop data-driven diagnostic, predictive, descriptive, optimization and risk stratification models.

I am keenly interested in knowledge-driven AI to support evidence-based decision making at the point-of-care. My research involves the digitization of health knowledge (such as clinical guidelines, psychosocial theories, clinical workflows, etc.) as high-level knowledge models that can be reasoned with patient data to deduce context-sensitive, evidence-based decision support recommendations. I am interested in semantic web technologies to model, represent and reason healthcare knowledge to both infer new knowledge and deduce knowledge-driven decision support solutions to aid practitioners and patients.

From a computer science perspective, the endpoint of my research interest is the formulation of new AI methods for data analytics (machine learning and data mining) and knowledge management (semantic web based knowledge modeling and reasoning). From a healthcare perspective, the endpoint of my research are innovative health informatics solutions targeting (a) point-of-care clinical decision support systems based on computerized clinical guidelines, (b) health data analytics enabling precision medicine, prediction of disease trends, health resource utilization, risk assessment and diagnostic decision support, (c) personalized patient empowerment services to assist chronic disease self-management, behaviour modification and ambient assisted living, and (d) personalized lifetime health, focusing on citizen health, to raise awareness of chronic disease risks.

I characterize my research program along the following themes:

1. Data-Driven AI
   a. Health data analytics for decision support, prediction and optimization
   b. Machine learning and data mining
   c. Predictive modeling
   d. Data visualization (Dashboards, scorecards, operational intelligence)
   e. Activity recognition for remote monitoring
2. Knowledge-Driven AI
   a. Semantic web (Modeling and Reasoning)
   b. Knowledge management—Computerization of clinical guidelines, behaviour change models, clinical workflows and risk assessment algorithm
3. Health Informatics (Digital Health, Medical Informatics, Clinical Informatics)
   a. Precision medicine
   b. Personalized lifetime health (Quantified self, wellness programs)
   c. Behaviour modification and self-management
   d. Clinical decision support systems
   e. Global health
   f. Mobile health

I lead the NICHE (kNowledge Intensive Computing for Healthcare Enterprises) research group. NICHE epitomizes the conceptual synergy between my research themes, and provides a unique research environment whereby researchers, professionals, postdocs and graduate students from both health informatics, computer science, health professions and medicine work in a collaborative environment to investigate and develop innovative health informatics solutions using cross-disciplinary solution approaches and methods.