Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

2026-09-16

AI Summary

为了解决遗传病严重程度分类的主观性和劳动密集性问题，研究开发了一种结合ReAct和RAG技术的AI代理，根据ACMG指南和ACOG标准自动分类疾病严重程度。

Abstract

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

Problem

• Genetic Disease Severity
• Classification
• Genomic Screening

Innovation

• Reasoning and Acting (ReAct)
• Retrieval-Augmented Generation (RAG)
• Human Phenotype Ontology
• Genetic Disease Severity Classification
• Evidence-Based