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Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

A framework for reconstructing clinical timelines from text and structured EHR data.

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Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

By Sayantan Kumar, Shahriar Noroozizadeh, Juyong Kim, Jeremy C. WeissarXiv
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The paper introduces a retrieval-augmented multimodal alignment framework to improve the temporal precision of absolute clinical timelines extracted from text.

It formulates timeline reconstruction as a graph-based multistep process, using both unstructured narratives and structured electronic health record (EHR) data. The approach is evaluated on the i2m4 benchmark, showing improved accuracy and concordance compared to unimodal text-only reconstruction.

Abstract

The paper introduces a retrieval-augmented multimodal alignment framework to improve the temporal precision of absolute clinical timelines extracted from text. It formulates timeline reconstruction as a graph-based multistep process, using both unstructured narratives and structured electronic health record (EHR) data. The approach is evaluated on the i2m4 benchmark, showing improved accuracy and concordance compared to unimodal text-only reconstruction.

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clinical timelinestext retrievalmultimodal alignmentstructured EHR datatemporal precisionKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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