Qwant Research @DEFT 2019: Document matching and information retrieval using clinical cases

07/06/2019
by   Estelle Maudet, et al.
0

This paper reports on Qwant Research contribution to tasks 2 and 3 of the DEFT 2019's challenge, focusing on French clinical cases analysis. Task 2 is a task on semantic similarity between clinical cases and discussions. For this task, we propose an approach based on language models and evaluate the impact on the results of different preprocessings and matching techniques. For task 3, we have developed an information extraction system yielding very encouraging results accuracy-wise. We have experimented two different approaches, one based on the exclusive use of neural networks, the other based on a linguistic analysis.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/27/2018

A Concept-Centered Hypertext Approach to Case-Based Retrieval

The goal of case-based retrieval is to assist physicians in the clinical...
research
05/24/2023

Leveraging LLMs for KPIs Retrieval from Hybrid Long-Document: A Comprehensive Framework and Dataset

Large Language Models (LLMs) demonstrate exceptional performance in text...
research
02/24/2023

Language Models are Few-shot Learners for Prognostic Prediction

Clinical prediction is an essential task in the healthcare industry. How...
research
12/01/2022

NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework

Information retrieval aims to find information that meets users' needs f...
research
12/29/2022

Maximizing Use-Case Specificity through Precision Model Tuning

Language models have become increasingly popular in recent years for tas...
research
04/25/2023

Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports

With the increasing number of clinical trial reports generated every day...
research
11/10/2021

Design Theory to improve health evidence retrieval

Objective: Our study objective is to design a feasible technology soluti...

Please sign up or login with your details

Forgot password? Click here to reset