First-order logic theory for manipulating clinical practice guidelines applied to comorbid patients: a case study

Area of Science:

• Medical Informatics
• Computational Medicine
• Artificial Intelligence in Healthcare

Background:

• Clinical practice guidelines (CPGs) optimize single-disease treatments but struggle with comorbid patients.
• Concurrent application of multiple CPGs can lead to adverse interactions, hindering clinical adoption.
• Existing research lacks a generalized framework to manage CPG interactions, considering temporal and ordering constraints.

Purpose of the Study:

• To propose a generalized, first-order logic (FOL)-based framework for mitigating adverse interactions in combined therapies for comorbid patients.
• To develop a meta-algorithm leveraging entailment properties for identifying and resolving CPG-induced conflicts.
• To demonstrate the framework's effectiveness and robustness using a case study.

Main Methods:

• Development of a first-order logic (FOL) model to represent CPGs and their interactions.
• Implementation of a meta-algorithm utilizing logical entailment for conflict detection and resolution.
• Application of the framework to a case study involving type 2 diabetes and rheumatoid arthritis comorbidities.

Main Results:

• The proposed FOL-based framework effectively identifies and mitigates adverse interactions between concurrently applied CPGs.
• The approach successfully handles complex nuances such as time and ordering requirements within CPGs.
• A case study demonstrated the framework's expressiveness and robustness in a realistic clinical scenario.

Conclusions:

• A generalized FOL-based framework offers a robust solution for mitigating adverse interactions in comorbid patient treatment.
• This approach enhances the clinical applicability of CPGs for complex patient cases.
• The framework provides a foundation for a generalized theory of CPG interaction mitigation.