Paragraph Retrieval for Enhanced Question Answering in Clinical Documents

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

Paragraph Retrieval for Enhanced Question Answering in Clinical Documents

Original language description

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

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

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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Number of pages

11

Pages from-to

580-590

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Bangkok, Thailand

Event date

Aug 16, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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