Denys Proux

Addressing Risk Assessment for Patient Safety in Hospitals through Information Extraction in Medical Reports

Hospital Acquired Infections (HAI) is a real burden for doctors and risk surveillance experts. The impact on patients’ health and related healthcare cost is very significant and a major concern even for rich countries. Furthermore required data to evaluate the threat is generally not available to experts and that prevents from fast reaction. However, recent advances in Computational Intelligence Techniques such as Information Extraction, Risk Patterns Detection in documents and Decision Support Systems allow now to address this problem.

Évaluation d’un outil d’aide á l’anonymisation des documents médicaux basé sur le traitement automatique du langage naturel

Anonymization of personal data is a legal requirement for their use as part of a research project. In the context of developing a tool for detecting hospital-acquired infections, 2000 medical documents were needed for the research project ALADIN. To help annotators to anonymize this corpus of documents, a tool for the anonymization has been developed, relying on Natural Language Processing techniques. The recall, precision and F-score of the automatic phase of the anonymizer were respectively 79.7, 85.2 and 82.4%. The gold- standard used for the evaluation was the manual anonymization of the documents. The performance of the automatic anonymization can still be improved but the tool is already a considerable help in this process in terms of saving time and in terms of quality of anonymization (including the accuracy of labeling anonymized terms and computation of time duration).

Muninn: a pragmatic information extraction system

Gathering data from scientific corpora to feed specialized databases has motivated the development of a computer system to help with extracting pertinent information from texts, relying on advanced linguistic tools, complete with object-oriented knowledge modeling capabilities. The methodology and algorithms proposed are under evaluation in genomics, where the goal is to automatically detect and extract information on gene interactions.