Tabula Rasa: A Case Study in Chronic Pain Management Using Individual-Centric AI.

Area of Science:

• * Artificial Intelligence
• * Computational Psychology
• * Health Informatics

Background:

• * Chronic health conditions are influenced by complex, individual-specific behavioral, social, and environmental factors.
• * Traditional population health approaches face challenges in tailoring treatments to individual symptomatology.
• * Personalized medicine requires adaptive systems capable of understanding unique patient experiences.

Purpose of the Study:

• * To introduce a non-axiomatic reasoning system (NARS) as an AI approach for personalized health management.
• * To demonstrate the application of NARS in the domain of chronic pain management.
• * To highlight NARS's capability for incremental and ongoing learning in adaptive treatment.

Main Methods:

• * Development and application of a non-axiomatic reasoning system (NARS).
• * Utilizing NARS for incremental learning and establishing associations between behaviors and pain exacerbation.
• * Demonstrating NARS's ability to dynamically revise the strength of learned associations over time.

Main Results:

• * NARS successfully identified and associated specific behavioral activities with increased pain levels in individuals.
• * The system demonstrated the capacity to adapt and refine these associations based on ongoing learning.
• * Validation of NARS's effectiveness in a chronic pain management context.

Conclusions:

• * Non-axiomatic reasoning systems (NARS) provide a viable AI framework for personalized chronic disease management.
• * NARS facilitates patient-centric adaptation by learning individual-specific symptom triggers.
• * The system shows broad potential for application in any chronic condition requiring tailored therapeutic strategies.