TreeText-CTS builds source-traceable tree-path evidence from multi-scale EHR windows via frozen XGBoost, selects subsets, and uses an LM encoder to reach top AUPRC among text-based interfaces on mortality and sepsis tasks while staying competitive with numerical models.
Set functions for time series
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cs.LG 2years
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UNVERDICTED 2representative citing papers
MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.
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TreeText-CTS: Compact, Source-Traceable Tree-Path Evidence for Irregular Clinical Time-Series Prediction
TreeText-CTS builds source-traceable tree-path evidence from multi-scale EHR windows via frozen XGBoost, selects subsets, and uses an LM encoder to reach top AUPRC among text-based interfaces on mortality and sepsis tasks while staying competitive with numerical models.
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MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.