clinical-time-series

CLIR-Bench is a benchmark for multimodal question answering over irregularly sampled clinical time series, constructed from ICU records with 6,600 QA instances across 11 clinical variables. It reveals that existing generalist models struggle with sparse temporal evidence, highlighting the need for stronger irregular time-series reasoning methods.

This paper presents CISM, a channel-independent spectrogram framework that treats missingness as a predictive signal for clinical multivariate time series prediction. Experiments on MIMIC-IV show it outperforms baselines for in-hospital mortality prediction.

Presents a diffusion-based approach for generating irregular clinical time series that jointly models laboratory values and their observation patterns, using the DACMI benchmark from MIMIC-III. The model captures clinically meaningful dependencies between patient physiology and testing behavior under MNAR-like missingness.

Introduces TreeText-CTS, a method that converts irregular EHR trajectories into compact, source-traceable tree-path evidence units without patient-level summarization. Achieves state-of-the-art AUROC and AUPRC among text-based EHR time-series interfaces on three clinical benchmarks.