Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis
Summary: arXiv:2604.05116v1 Announce Type: new
Abstract: Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths.
To address these challenges, researchers have proposed a novel framework known as Latent Diagnostic Trajectory Learning (LDTL). This framework integrates a planning LLM agent and a diagnostic LLM agent to improve the accuracy and efficiency of clinical diagnoses.
Key Components of the LDTL Framework
The LDTL framework is structured around two primary components:
- Diagnostic LLM Agent: This agent treats diagnostic action sequences as latent paths. It introduces a posterior distribution that emphasizes trajectories that yield more significant diagnostic information, effectively guiding the diagnostic process.
- Planning LLM Agent: This agent is trained to adhere to the posterior distribution established by the diagnostic LLM agent. Its role is to foster coherent diagnostic trajectories that progressively diminish uncertainty throughout the diagnostic process.
Significance of the Approach
The implementation of the LDTL framework offers several advantages:
- Enhanced Diagnostic Accuracy: Experimental results on the MIMIC-CDM benchmark indicate that the LDTL framework outperforms existing diagnostic systems, achieving higher diagnostic accuracy in a sequential clinical diagnosis context.
- Reduced Need for Diagnostic Tests: By optimizing the evidence acquisition process, the LDTL framework requires fewer diagnostic tests to arrive at accurate conclusions, thus streamlining clinical workflows.
- Trajectory-Level Posterior Alignment: Ablation studies within the research highlight the pivotal role of trajectory-level posterior alignment in realizing these accuracy improvements, showcasing the effectiveness of the proposed model.
Conclusion
The introduction of the Uncertainty-Guided Latent Diagnostic Trajectory Learning framework represents a significant advancement in the field of clinical diagnosis. By effectively modeling the sequential acquisition of clinical evidence under uncertainty, this innovative approach not only improves diagnostic accuracy but also enhances the overall efficiency of clinical practice. As the healthcare landscape continues to evolve, frameworks like LDTL may play a critical role in shaping the future of diagnostic methodologies.