Mental health assessment: Inference, explanation, and coherence.

Area of Science:

• Cognitive Science
• Psychiatry
• Artificial Intelligence

Background:

• Mental health professionals diagnose disorders by finding the best explanation for patient symptoms.
• This diagnostic process involves comparing competing hypotheses against available evidence.
• Current methods may lack a systematic framework for evaluating explanatory power.

Purpose of the Study:

• To introduce explanatory coherence as a framework for mental health assessment.
• To demonstrate the computational model ECHO for assessing explanatory coherence.
• To compare explanatory coherence with Bayesian inference and hermeneutic interpretation in clinical contexts.

Main Methods:

• Utilized the theory of explanatory coherence, outlining 7 principles for hypothesis evaluation.
• Applied the computational model ECHO to efficiently compute explanatory coherence.
• Modeled psychiatric interviewing and psychotherapeutic evaluation cases.

Main Results:

• Demonstrated the practical application of explanatory coherence in mental health assessment.
• Showcased the ECHO model's capability in handling complex clinical data.
• Provided a computational approach to inference to the best explanation in psychiatry.

Conclusions:

• Explanatory coherence offers a robust and plausible framework for mental health diagnosis.
• The ECHO model provides an efficient computational tool for applying this theory.
• This approach is presented as a more viable alternative to existing methods like Bayesian inference.