No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery
Journal:
arXiv
Published Date:
May 22, 2025
Abstract
Deep learning models trained on extensive Electronic Health Records (EHR)
data have achieved high accuracy in diagnosis prediction, offering the
potential to assist clinicians in decision-making and treatment planning.
However, these models lack two crucial features that clinicians highly value:
interpretability and interactivity. The ``black-box'' nature of these models
makes it difficult for clinicians to understand the reasoning behind
predictions, limiting their ability to make informed decisions. Additionally,
the absence of interactive mechanisms prevents clinicians from incorporating
their own knowledge and experience into the decision-making process. To address
these limitations, we propose II-KEA, a knowledge-enhanced agent-driven causal
discovery framework that integrates personalized knowledge databases and
agentic LLMs. II-KEA enhances interpretability through explicit reasoning and
causal analysis, while also improving interactivity by allowing clinicians to
inject their knowledge and experience through customized knowledge bases and
prompts. II-KEA is evaluated on both MIMIC-III and MIMIC-IV, demonstrating
superior performance along with enhanced interpretability and interactivity, as
evidenced by its strong results from extensive case studies.
