Paragraph Retrieval for Enhanced Question Answering in Clinical Documents

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492879" target="_blank" >RIV/00216208:11320/24:10492879 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.bionlp-1.48/" target="_blank" >https://aclanthology.org/2024.bionlp-1.48/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Paragraph Retrieval for Enhanced Question Answering in Clinical Documents

Popis výsledku v původním jazyce

Healthcare professionals often manually extract information from large clinical documents to address patient-related questions. The use of Natural Language Processing (NLP) techniques, particularly Question Answering (QA) models, is a promising direction for improving the efficiency of this process. However, document-level QA from large documents is often impractical or even infeasible (for model training and inference). In this work, we solve the document-level QA from clinical reports in a two-step approach: first, the entire report is split into segments and for a given question the most relevant segment is predicted by a NLP model; second, a QA model is applied to the question and the retrieved segment as context. We investigate the effectiveness of heading-based and naive paragraph segmentation approaches for various paragraph lengths on two subsets of the emrQA dataset. Our experiments reveal that an average paragraph length used as a parameter for the segmentation has no significant effect on p

Název v anglickém jazyce

Paragraph Retrieval for Enhanced Question Answering in Clinical Documents

Popis výsledku anglicky

Healthcare professionals often manually extract information from large clinical documents to address patient-related questions. The use of Natural Language Processing (NLP) techniques, particularly Question Answering (QA) models, is a promising direction for improving the efficiency of this process. However, document-level QA from large documents is often impractical or even infeasible (for model training and inference). In this work, we solve the document-level QA from clinical reports in a two-step approach: first, the entire report is split into segments and for a given question the most relevant segment is predicted by a NLP model; second, a QA model is applied to the question and the retrieved segment as context. We investigate the effectiveness of heading-based and naive paragraph segmentation approaches for various paragraph lengths on two subsets of the emrQA dataset. Our experiments reveal that an average paragraph length used as a parameter for the segmentation has no significant effect on p

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

ISBN

979-8-89176-130-8

ISSN

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e-ISSN

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Počet stran výsledku

11

Strana od-do

580-590

Název nakladatele

Association for Computational Linguistics

Místo vydání

Stroudsburg, PA, USA

Místo konání akce

Bangkok, Thailand

Datum konání akce

16. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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