ClinPRISM: Multimodal LLM Framework for Clinical Time-Series QA
A research paper on arXiv introduces ClinPRISM, a cost-effective multimodal large language model (LLM) reasoning framework designed for question answering over irregular clinical time series (ICTS) data. The framework addresses challenges in modeling sparsity, asynchrony, and irregular sampling patterns in clinical observations. It features an irregularity-aware multi-scale encoder to capture sparse clinical evidence across diverse temporal scales, a temporal evidence distiller to integrate representations and compress them into LLM-compatible tokens, and a progressive alignment strategy to align irregular trajectories with the LLM's textual embedding space. The paper is identified by arXiv ID 2607.25947.
Key facts
- ClinPRISM is a multimodal LLM reasoning framework for ICTS question answering.
- It addresses sparsity, asynchrony, and irregular sampling in clinical data.
- Includes an irregularity-aware multi-scale encoder.
- Uses a temporal evidence distiller for token compression.
- Implements progressive alignment with LLM embedding space.
- Paper published on arXiv with ID 2607.25947.
- Focuses on cost-effective healthcare AI applications.
- Enhances general-purpose time-series LLMs for clinical use.
Entities
Institutions
- arXiv