Domain knowledge
Definitions, criteria, questions, exclusions, comorbidities, treatments and relevant clinical context.
Domain knowledge, causal reasoning, fuzzy evaluation and learning work together within Ad-Infer to give clinical meaning to new information.
EVAL-KBS is the knowledge-processing and inferential framework within Ad-Infer. It connects domain knowledge with Type-2 causal reasoning, Fuzzy Evaluation Learning, deep-learning mechanisms and Bayesian belief updating.
Clinical knowledge is represented through objects, premises, relationships, definitions, exclusions and weighted rules. Graded fuzzy states preserve frequency, intensity, duration and uncertainty when observations are more informative than a binary value.
Definitions, criteria, questions, exclusions, comorbidities, treatments and relevant clinical context.
Causal relationships and temporal context determine how information supports or challenges competing hypotheses.
Graded evidence, adaptive weights, learning processes and Bayesian updating contribute to the evolving belief state.
Sleep-EVAL organizes the sleep-domain knowledge used by Ad-Infer: sleep-wake schedules, symptoms, diagnostic criteria, exclusions, treatments, related medical and psychiatric conditions, impairment and function. Ad-Infer applies its inferential architecture to this content.
The integrated LLM supports questions, clarifications and semantic interpretation. EVAL-KBS re-evaluates the returned information, including its relationship to direct and indirect causality, before Ad-Infer updates a hypothesis or determines the next question.
Specialized knowledge bases can be configured for different medical and research questions while preserving explicit definitions, reasoning paths and longitudinal comparability. Changes in knowledge and learned relationships are evaluated for their intended use.