Science & medicine
Connect clinical definitions, symptom patterns, multidomain burden and longitudinal change within a coherent research framework.
Science & medicine
EVALRI combines clinical science, epidemiology, computational methods, and longitudinal research. Its assessment tools are powered by Ad-Infer, a hybrid clinical AI architecture developed by Maurice M. Ohayon beginning in 1987.
Ad-Infer integrates Hopfield neural computation, Zadeh fuzzy logic, non-monotonic causal reasoning, Bayesian learning, and explicit diagnostic knowledge. This architecture supports standardized, interpretable phenotyping across sleep medicine, psychiatry, neurology, and related medical domains.
EVALRI has conducted population-based and longitudinal studies in North America, Europe, and Asia and has contributed to more than 200 peer-reviewed publications. Its collaborations include academic investigators, health organizations, and industry partners.
The current Ad-Infer architecture integrates an LLM as an internal functional component. Its questions and semantic interpretation are directed by clinical hypotheses; responses and interaction evidence return to EVAL-KBS for causal re-evaluation, fuzzy processing and belief updating.
Explore the inferential framework
See how Ad-Infer links clinical knowledge, causal reasoning, integrated language functions and longitudinal evidence.