Summary of Multimodal Clinical Reasoning Through Knowledge-augmented Rationale Generation, by Shuai Niu et al.
Multimodal Clinical Reasoning through Knowledge-augmented Rationale Generation
by Shuai Niu, Jing Ma, Liang Bai, Zhihua Wang, Yida Xu, Yunya Song, Xian Yang
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose ClinRaGen, a smaller language model (SLM) optimized for multimodal rationale generation in disease diagnosis. The SLM incorporates a knowledge-augmented attention mechanism to merge domain knowledge with time series electronic health records (EHRs), using a stepwise rationale distillation strategy to produce both textual and time-series based clinical rationales. The authors highlight the limitations of current large language models (LLMs) in processing domain-specific knowledge and propose ClinRaGen as a solution to bridge this gap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve disease diagnosis by developing a smaller language model that can generate accurate clinical rationales using multimodal data, such as electronic health records. The new model, called ClinRaGen, combines domain knowledge with time-series data to produce textual and time-series based explanations for diseases. This could lead to more reliable diagnoses and better healthcare. |
Keywords
» Artificial intelligence » Attention » Distillation » Language model » Time series
