Automatic detection of surgical site infections from a clinical data warehouse

Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital of Bordeaux. Our goal was to develop an automatic detection algorithm based on hospital information system data. Three years (2015, 2016 and 2017) of manual spine surgery monitoring have been used as a gold standard to extract features and train machine learning algorithms. The dataset contained 22 SSIs out of 2133 spine surgeries. Two different approaches were compared. The first used several data sources and achieved the best performance but is difficult to generalize to other institutions. The second was based on free text only with semiautomatic extraction of discriminant terms. The algorithms managed to identify all the SSIs with 20 and 26 false positives respectively on the dataset. Another evaluation is underway. These results are encouraging for the development of semi-automated surveillance methods.

IAM at CLEF eHealth 2018: Concept Annotation and Coding in French Death Certificates

In this paper, we describe the approach and results for our participation in the task 1 (multilingual information extraction) of the CLEF eHealth 2018 challenge. We addressed the task of automatically assigning ICD-10 codes to French death certificates. We used a dictionary-based approach using materials provided by the task organizers. The terms of the ICD-10 terminology were normalized, tokenized and stored in a tree data structure. The Levenshtein distance was used to detect typos. Frequent abbreviations were detected by manually creating a small set of them. Our system achieved an F-score of 0.786 (precision: 0.794, recall: 0.779). These scores were substantially higher than the average score of the systems that participated in the challenge.