Research Overview
Ysmalal conducts interdisciplinary research at the intersection of artificial intelligence, clinical medicine, epidemiology, health outcomes research, and real-world evidence. Our goal is to develop, validate, and monitor AI systems that improve patient outcomes while ensuring safety, fairness, transparency, and responsible implementation.
We focus on translating data into actionable insights that support healthcare decision-making and improve population health.
Core Research Areas
Artificial Intelligence and Machine Learning
- Clinical prediction models
- Explainable artificial intelligence (XAI)
- Risk stratification and outcome prediction
- Predictive analytics using electronic health records
- Real-world machine learning applications
Drug Safety and Pharmacovigilance
- Adverse drug event detection
- Post-marketing safety surveillance
- AI-assisted pharmacovigilance
- Medication safety monitoring
- Real-world drug safety assessment
Real-World Evidence and Health Outcomes Research
- Comparative effectiveness research
- Treatment outcome evaluation
- Healthcare utilization studies
- Population health analytics
- Patient-centered outcomes research
Chronic Disease Prediction and Management
- Diabetes management
- Obesity research
- Cardiovascular disease prediction
- Behavioral health outcomes
- Personalized treatment strategies
Responsible and Trustworthy AI
- Bias and fairness assessment
- Algorithm transparency
- Model validation
- Ethical AI implementation
- AI governance and oversight