Louise Deléger

Extracting medical information from narrative patient records: the case of medication-related information

OBJECTIVE While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction. DESIGN Our approach relies on a semantic lexicon and extraction rules as a two-phase strategy: first, drug names are recognized and, then, the context of these names is explored to extract drug-related information (mode, dosage, etc) according to rules capturing the document structure and the syntax of each kind of information. Different configurations are tested to improve this baseline system along several dimensions, particularly drug name recognition-this step being a determining factor to extract drug-related information. Changes were tested at the level of the lexicons and of the extraction rules. RESULTS The initial system participating in i2b2 achieved good results (global F-measure of 77%). Further testing of different configurations substantially improved the system (global F-measure of 81%), performing well for all types of information (eg, 84% for drug names and 88% for modes), except for durations and reasons, which remain problematic. CONCLUSION This study demonstrates that a simple rule-based system can achieve good performance on the medication extraction task. We also showed that controlled modifications (lexicon filtering and rule refinement) were the improvements that best raised the performance.

Overview of the Bacteria Biotope Task at BioNLP Shared Task 2016.

This paper presents the Bacteria Biotope task of the BioNLP Shared Task 2016, which follows the previous 2013 and 2011 editions. The task focuses on the extraction of the locations (biotopes and geographical places) of bacteria from PubMe abstracts and the characterization of bacteria and their associated habitats with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on bacteria habitats for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, the challenge organization, and the evaluation metrics. We also provide an analysis of the results obtained by participants.

Overview of the Regulatory Network of Plant Seed Development (SeeDev) Task at the BioNLP Shared Task 2016.

This paper presents the SeeDev Task of the BioNLP Shared Task 2016. The purpose of the SeeDev Task is the extraction from scientific articles of the descriptions of genetic and molecular mechanisms involved in seed development of the model plant, Arabidopsis thaliana. The SeeDev task consists in the extraction of many different event types that involve a wide range of entity types so that they accurately reflect the complexity of the biological mechanisms. The corpus is composed of paragraphs selected from the full-texts of relevant scientific articles. In this paper, we describe the organization of the SeeDev task, the corpus characteristics, and the metrics used for the evaluation of participant systems. We analyze and discuss the final results of the seven participant systems to the test. The best F-score is 0.432, which is similar to the scores achieved in similar tasks on molecular biology.

Personalized and automated remote monitoring of atrial fibrillation

AIMS Remote monitoring of cardiac implantable electronic devices is a growing standard; yet, remote follow-up and management of alerts represents a time-consuming task for physicians or trained staff. This study evaluates an automatic mechanism based on artificial intelligence tools to filter atrial fibrillation (AF) alerts based on their medical significance. METHODS AND RESULTS We evaluated this method on alerts for AF episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the patient health record to a digital version, then a knowledge-based algorithm based on an applied formal ontology allows to calculate the CHA2DS2-VASc score and evaluate the anticoagulation status of the patient. Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with human expert analysis by two physicians. A total of 1783 alerts about AF episode >5 min in 60 patients were processed. A 1749 of 1783 alerts (98%) were adequately classified and there were no underestimation of alert importance in the remaining 34 misclassified alerts. CONCLUSION This work demonstrates the ability of a pilot system to classify alerts and improves personalized remote monitoring of patients. In particular, our method allows integration of patient medical history with device alert notifications, which is useful both from medical and resource-management perspectives. The system was able to automatically classify the importance of 1783 AF alerts in 60 patients, which resulted in an 84% reduction in notification workload, while preserving patient safety.

Paraphrase Detection in Monolingual Specialized/Lay Comparable Corpora

Paraphrases are a key feature in many natural language processing applications, and their extraction and generation are important tasks to tackle. Given two comparable corpora in the same language and the same domain, but displaying two different discourse types (lay and specialized), specific paraphrases can be spotted which provide a dimension along which these discourse types can be contrasted. Detecting such paraphrases in comparable corpora is the goal of the present work. Generally, paraphrases are identified by means of lexical and/or structural patterns. In this chapter, we present two methods to extract paraphrases across lay and specialized French monolingual comparable corpora. The first method uses lexical patterns designed according to intuition and linguistic studies, while the second is empirical, based on n-gram matching. The two methods appear to be complementary: the n-gram method confirms the initial lexical patterns and identifies other patterns. Besides, differences in the direction of application of paraphrase patterns highlight differences between specialized and lay discourse.