Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance

<p>Abstract</p> <p>Background</p> <p>The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support system to detect suspected cases, based on the principle of syndromic surveillance, is being developed at the University of Lyon's Hôpital de la Croix-Rousse. This tool will analyse structured data and narrative reports from computerized emergency department (ED) medical records. The first step consists of developing an application (UrgIndex) which automatically extracts and encodes information found in narrative reports. The purpose of the present article is to describe and evaluate this natural language processing system.</p> <p>Methods</p> <p>Narrative reports have to be pre-processed before utilizing the French-language medical multi-terminology indexer (ECMT) for standardized encoding. UrgIndex identifies and excludes syntagmas containing a negation and replaces non-standard terms (abbreviations, acronyms, spelling errors...). Then, the phrases are sent to the ECMT through an Internet connection. The indexer's reply, based on Extensible Markup Language, returns codes and literals corresponding to the concepts found in phrases. UrgIndex filters codes corresponding to suspected infections. Recall is defined as the number of relevant processed medical concepts divided by the number of concepts evaluated (coded manually by the medical epidemiologist). Precision is defined as the number of relevant processed concepts divided by the number of concepts proposed by UrgIndex. Recall and precision were assessed for respiratory and cutaneous syndromes.</p> <p>Results</p> <p>Evaluation of 1,674 processed medical concepts contained in 100 ED medical records (50 for respiratory syndromes and 50 for cutaneous syndromes) showed an overall recall of 85.8% (95% CI: 84.1-87.3). Recall varied from 84.5% for respiratory syndromes to 87.0% for cutaneous syndromes. The most frequent cause of lack of processing was non-recognition of the term by UrgIndex (9.7%). Overall precision was 79.1% (95% CI: 77.3-80.8). It varied from 81.4% for respiratory syndromes to 77.0% for cutaneous syndromes.</p> <p>Conclusions</p> <p>This study demonstrates the feasibility of and interest in developing an automated method for extracting and encoding medical concepts from ED narrative reports, the first step required for the detection of potentially infectious patients at epidemic risk.</p

Bibliothèques et sciences de l\u27information : quel dialogue ? - Programme

Face aux évolutions technologiques, scientifiques, économiques, sociales, culturelles et politiques de leur environnement, les bibliothèques en tant qu\u27organismes culturels et scientifiques doivent repenser leurs pratiques, leur positionnement économique, politique et institutionnel, et leur rôle social, culturel et scientifique. Dans ce contexte, qu\u27attendent les bibliothèques de la recherche ? Quels thématiques et projets de recherche répondraient à leurs besoins ? Les sciences de l\u27information peuvent-elles apporter des réponses aux enjeux actuels ? Pour répondre aux interrogations posées par ces nouveaux défis, le colloque croise les approches et expériences de bibliothécaires et chercheurs en sciences de l\u27information de nombreux pays ( France, Canada, Etats-Unis, Allemagne, Royaume-Uni...

Evaluation de différentes méthodes de repérage des infections nosocomiales dans un service de chirurgie et un service d'obstétrique des Hospices Civils de Lyon

LYON1-BU Santé (693882101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Deep learning versus conventional machine learning for detection of healthcare-associated infections in French clinical narratives

International audienceObjective: The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. Methods: The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals. Each of them was labeled as presence (1) or absence (0) of HAI. We started by normalizing the records using a list of preprocessing techniques. We calculated an overall performance metric, the F1 Score, to compare a deep learning method (convolutional neural network [CNN]) with the most popular conventional ML models (Bernoulli and multi-naïve Bayes, k-nearest neighbors, logistic regression, random forests, extra-trees, gradient boosting, support vector machines). We applied the hyperparameter Bayesian optimization for each model based on its HAI identification performances. We included the set of text representation as an additional hyperparameter for each model, using four different text representations (bag of words, term frequency–inverse document frequency, word2vec, and Glove). Results: CNN outperforms all other conventional ML algorithms for HAI classification. The best F1 Score of 97.7% ± 3.6% and best area under the curve score of 99.8% ± 0.41% were achieved when CNN was directly applied to the processed clinical notes without a pretrained word2vec embedding. Through receiver operating characteristic curve analysis, we could achieve a good balance between false notifications (with a specificity equal to 0.937) and system detection capability (with a sensitivity equal to 0.962) using the Youden's index reference. Conclusions: The main drawback of CNNs is their opacity. To address this issue, we investigated CNN inner layers' activation values to visualize the most meaningful phrases in a document. This method could be used to build a phrase-based medical assistant algorithm to help the infection control practitioner to select relevant medical records. Our study demonstrated that deep learning approach outperforms other classification learning algorithms for automatically identifying HAIs in medical reports

Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?

International audienc

Evaluation de sept réactifs d'immunochromatographie pour détecter les rotavirus humains dans les selles

International audienc

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 Computationa