About EVALRI
An independent California-based research organization connecting clinical science, epidemiology, computational methods and nearly four decades of Ad-Infer development.
About EVALRI
EVAL Research Institute (EVALRI) is an independent research organization based in California. It combines clinical science, epidemiology, computational methods, and nearly four decades of experience developing structured clinical AI and longitudinal research programs.
EVALRI develops evidence-based data solutions and customized assessment tools for medicine, public health, and research. Its work spans sleep medicine, psychiatry, neurology, epidemiology, gastroenterology, and related areas and has contributed to more than 200 peer-reviewed publications.
The Institute’s central computational platform is Ad-Infer, a causal inferential architecture designed for standardized interviewing, phenotyping, longitudinal assessment, and clinically interpretable reasoning.
Ad-Infer was developed by Maurice M. Ohayon beginning in 1987. Maurice M. Ohayon personally holds all rights in Ad-Infer and its associated intellectual property. EVAL Research Institute holds no ownership rights in Ad-Infer. Its evolution now includes an integrated LLM directed by the inferential engine, with continuous return of interaction evidence to EVAL-KBS.
An evolving inferential platform
- 1987
Development begins
Maurice M. Ohayon begins developing Ad-Infer for clinical interviewing and inferential reasoning.
- 1992
Computational foundations
Doctoral work formalizes the architecture, including Hopfield neural computation and Zadeh fuzzy logic.
- 1990s onward
Population & longitudinal research
Domain knowledge bases support structured assessment in population samples, clinical groups and repeated interviews.
- Since 2016
Web-based assessment
Research applications extend the structured interview to web-based assessments.
- Current architecture
Integrated language-model capabilities
The evolving Ad-Infer system incorporates an LLM as an internal functional component directed by causal inference, fuzzy evaluation and the current belief state.
Explore the inferential framework
See how Ad-Infer links clinical knowledge, causal reasoning, integrated language functions and longitudinal evidence.