Automatic Detection of Surgical Site Infections in Neurosurgery in Rennes, France

1. Full-text automatic detection of Surgical Site Infections secondary to neurosurgery in Rennes, France Boris Campillo-Gimenez a, Nicolas Garcelona, Pascal Jarnob, Jean Marc Chapplainc, Marc Cuggia a a INSERM U936, Laboratory of medical informatics, University of Rennes 1, FRANCE b CCLIN Ouest, HôtelDieu, University hospital of Rennes, FRANCE c Department of hospital hygiene, University hospital of Rennes, FRANCE Paper Session - Intelligent Data Analysis - II Wednesday Afternoon Sessions - 15:45 to 17:15 (Room 2)

2. Context & Objective:Automatic detection of HAI B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1 • Context: • Need of detection and report of healthcare acquired infection (HAI) • Relying on Self-reporting by the clinicians • Under-reporting of these HAI • In Literature: • Coded and Structured data • Full-text (in French) ? • Objective: • Build a system to detect HAI • Using free-text documents

3. Constraint & Hypothesis:Full-text Automatic detection of HAI B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1 • Constraint related to the Full-text approach: • No standardization of HAI denomination in medical reports, • And the vocabulary related to HAI: fever, fever after surgery, wound festering … • Hypothesis: • To detect HAI • From Free-text documents • Using a Case-based similarity approach • To deal with semantic heterogeneity

4. Materials: Visualization Textualsimilarity Search Engine SSI CDW « R-oogle » HIS B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1

5. Method: A 3-Step Processing System 1 Nomindex1 2 Vector space Model 3 New SSI cases B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1 • Natural Language Processing: • Indexing of the documents • Semantic enrichment of the documents • Using UMLS • Document similarities: • Using a VSM and Cosine Similarity measure • Automatic learning system • By integrating concepts of the new validated cases

6. Method:From Documents to a Similarity Measure Specific of SSI No SSI SSI New cases INDEXING List of concepts List of concepts List of concepts List of concepts + synonyms + ancestors List of concepts + synonyms + ancestors List of concepts + synonyms + ancestors SEMANTIC ENRICHMENT FILTERING SSI concepts – No SSI concepts List of concepts specific to the SSI SSI vector α New Case vector B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1

7. Method:Evaluation Protocol Full-Text medical reports Evaluation framework: Recall, Precision … Patients of Neurosurgery in 2008-2009 DRG database Conventional surveillance SSI Indexing & Sem. Enrich Vector Space Model CDW Filtering No SSI Patients of Neurosurgery in 2010 New cases B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1

8. Results:Population study Full-Text medical reports ~ 4000 3 230 30 2.8M full-text documents and 841K patients Evaluation framework (Recall, Precision, Fmeasure) Patients of Neurosurgery in 2008-2009 DRG database Conventional surveillance 42 SSI Vector Space Model CDW Indexing & Sem. Enrich Filtering 400 No SSI Patients of Neurosurgery in 2010 New cases 1225 B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1

9. Results:Concept selection > 20% < 30% B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1

10. Results:Performance measures 8 Nosocomial Infections (but not SSI) B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1

11. Discussion – Conclusion B. Campillo-Gimenez, INSERM U936, CHU de Rennes, University of Rennes 1 • Automatic system for detection of SSI • Using full-text documents, it’s feasible • Using full-text, it’s efficient • Limitation: • 1 hospital, 1 surgery department • Advantages: • Full-text documents : Large source of information • Flexibility & Scalability: Quantitative approach & Learning process • Perspectives: • Associations with structured data • Optimizing NLP processing