Translating patient-related narratives into SNOMED-CT to enable interoperability of healthcare data SNOMED-CT representation Radiologic report Admission Letter Discharge Letter Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) offers a very large semantic combinatorial coverage and can be used to represent any medical concept. Electronic Health Records (EHRs) are principally composed of: Structured data Narrative data (free text) Narrative data contains crucial semantic and conceptual information. However, the major part of narrative data is not reused. 122446006 |Vitamin measurement 243796009 |situation with explicit context|: Refinement 276625007 |Level = #70 246090004 |Associated finding = 165645009 |Serum vitamin B12 low Refinement 246514001 |Units = = 258804004 |Nanogram/liter “Translating” narratives into SNOMED-CT would bring unprecedented possibilities for primary and secondary usage. Goal: automatic translation of narratives into SNOMED-CT sentences Corpus processing using Natural Language Processing (NLP) tools Fips syntactic parser Unitex corpus processor NLP Tools Automatic SNOMED-CT Translation De-identification of Protected Health Information (PHI) following HIPAA rules using finite state automata Corpus of de-identified French narratives Syntactico-semantic analysis SNOMED-CT representation Radiologic report Admission Letter Discharge Letter Creation of a corpus of patient related narratives extracted from EHRs Information Extraction (IE) Monsieur Blavignac a été transféré aux Hôpitaux Universitaires de Genève le 6 décembre 2012. Monsieur Nompatient a été transféré à l’Hôpital le 30 février 2012. Named-Entity Recognition (NER) Monolingual electronic dictionaries Construction of lexico-semantic resources of French medical terms followed by morphological, semantic and syntactic features Evaluation of the performance of the translation tool Gold standard corpus Creation of a gold standard corpus Bilingual electronic dictionaries Local grammars Manual translation into SNOMED-CT SNOMED-CT Translation Conclusion Automatic annotation of a corpus of 11’000 discharge summaries Results Examples of annotated sentences Annotation evaluation 5 discharge summaries (1’820 words) manually annotated with SNOMED-CT concepts by an expert By mapping the equivalences between simple terms, collocations and SNOMED- CT concepts, 421 medical terms were automatically annotated. First evaluation results (perfect match): Precision: 0,7173 Recall: 0,5171 F-score: 0,6009 Remarks The automatic translation of medical free text into SNOMED-CT is a demanding and cost- effective task Constructing lexico-semantic resources is a mandatory step to process medical natural language Automatic post-coordination can be performed by implementing rules derived from the SNOMED-CT compositional grammar in a syntactico-semantic parser. Next steps Improvement of the post-coordination Processing of the negation, e.g. Il n'y a pas de signe d'insuffisance circulatoire Enrichment of the lexico-semantic resources Enrichment of the corpora Evaluation of translation Corpus size (words) 4’481’191 Annotated terms 892’787 Unique SNOMED-CT concepts 7’569 Annotated terms per sentence 4.17 Un cathétérisme cardiaque droit sera réalisé chez le patient. SNOMED-CT code French term 40403005 cathétérisme du cœur droit 398166005 réalisé 116154003 patient Post-coordination Example of automatic translation after partial implementation of post- coordination rules following SNOMED-CT compositional grammar. Le patient présente une plaie du pied droit. 13924000| plaie Le patient prend des anti-inflammatoires, traitement qui est poursuivi durant l'hospitalisation. SNOMED-CT code French term 116154003 patient 373283003 anti-inflammatoire 266714009 poursuivre le traitement 32485007 hospitalisation 363698007| finding site 56459004| pied {116154003 | patient | }, {13924000 | plaie | : 363698007 | finding site | = 56459004 | pied | : 72741003 | laterality | = 262185006 | droit | } 72741003| laterality 262185006| droit Christophe GAUDET-BLAVIGNAC, Bmed, Mmed, BSc CS Vasiliki FOUFI, PhD Eric WEHRLI, PhD Christian LOVIS, MD MPH