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Updated: Apr 6, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Multilingual event extraction for epidemic detection.

Gaël Lejeune1, Romain Brixtel2, Antoine Doucet3

  • 1Groupe de Recherche en Informatique, Image et Instrumentation, University of Caen Lower-Normandy, boulevard Maréchal Juin, 14032 Caen, France; Laboratoire d'Informatique de Nantes Atlantique, University of Nantes, 2 rue de la Houssinière, 44322 Nantes, France.

Artificial Intelligence in Medicine
|August 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces the Daniel system, a multilingual news surveillance tool for tele-epidemiology. It extracts epidemic events across languages using character-level analysis, improving global disease detection timeliness.

Keywords:
Early event detectionEpidemic surveillanceMultilingual information accessPoorly endowed languagesTele-epidemiology

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Area of Science:

  • Computational epidemiology
  • Natural Language Processing
  • Public Health Informatics

Background:

  • Multilingual approaches enhance the timeliness of epidemic event detection globally.
  • Existing event extraction systems often require language-specific resources, limiting their scope.
  • Tele-epidemiology surveillance benefits from rapid, cross-lingual information processing.

Purpose of the Study:

  • To present the Daniel system, a multilingual news surveillance system for tele-epidemiology.
  • To demonstrate a system capable of extracting epidemic events in potentially any language.
  • To address the limitations of language-specific resources in event extraction.

Main Methods:

  • The Daniel system utilizes repetition and saliency common in journalistic writing.
  • Wikipedia provides seed disease names for matching repeated character strings.
  • Character-level text processing handles language variations and diverse writing systems, including declensions.

Main Results:

  • The Daniel system achieved an average F-measure of 82% across 5 tested languages (Chinese, English, Greek, Polish, Russian).
  • Performance on the BEcorpus (English) reached 87%, slightly below highly specialized systems.
  • Consistent performance across multiple languages contributes to improved epidemiological event detection reactivity and coverage.

Conclusions:

  • The Daniel system requires minimal resources (disease names, locations) for broad language coverage, unlike resource-intensive systems.
  • Its character-based approach is effective for morphologically rich and low-resourced languages.
  • The system offers unique geographic and linguistic coverage for epidemic surveillance, complementing existing methods.