During 1999-2003, While at Stanford, I was part of the InterMed Collaboratory (Stanford, Harvard, Columbia) which developed the GLIF3 computer-interpretable guideline (CIG) modeling language, and enactment engine. I implemented a diabetic foot guideline using GLIF3 at RAMBAM hospital.

In this ISF project (2016-9) we are developing a framework for combining Computer-interpretable guidelines with OWL medical ontologies to plan therapy for patients with multimorbidity. See our AMIA paper. This project is now continuing as part of CAPABLE (see below).

2018-2022 Read about our new project in the Press

Children with developmental disorders often have multiple diagnoses, which progress over time. Aided by a literature-based ontology, we used Self-Organizing Maps to detect clusters of children with particular multiple diagnoses and then applied Sequence Mining to find their progression.

Our group's mission is to use data science methods in order to improve people's health by developing methodologies and tools that provide decision-support to patients and their care providers. To do so, we combine methods for knowledge representation and reasoning, data management, and statistical inference and reasoning and apply it to large longitudinal multidimensional health data sets that are continuously updated. more

Clinical guidelines may recommend the same treatment for different patients. But some subpopulations may not benefit from the first-line treatment (e.g., antibiotic A) but from a different treatment (Anti-c). Can we use machine learning to cluster similar patients belonging to different clinical context? These can be different wellbeing states (good vs. poor wellbeing). Can we predict the most effective care process for patients belonging to a certain context?

"SHOMER MASSA": Safeguarding and promoting mental health and resilience among adolescents and young adults using mobile technology. Collaboration with Prof. Danny Koren from Psychology Dept. and graduate students Ofri Ben-Yehuda and Efrat Dreazen.