Marie Dupuch, Frédérique Segond, André Bittar, Luca Dini, Lina Soualmia, Stefan Darmoni, Quentin Gicquel, Marie-Hélène Metzger

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1 Separate the grain from the chaff: designing a system to make the best use of language and knowledge technologies to model textual medical data extracted from electronic health records Marie Dupuch, Frédérique Segond, André Bittar, Luca Dini, Lina Soualmia, Stefan Darmoni, Quentin Gicquel, Marie-Hélène Metzger

2 General context : Health goes to digital and becomes ehealth Electronic medical record (EMR) data (semantic) mining: support medical decision epidemiological surveillance However, today, most data are still unstructured and very few are coded in the EMR Only data needed for pricing is systematically structured and coded in hospital medical records. Synodos is a follow-up to the ALADIN project (ANR TecSan 2008) that provided a feasibility study and a proof of concept good overall performance of NLP for detection of hospital acquired infections Necessity to distinguish between different processing level: linguistics, knowledge representation and expert knowledge 2

3 Synodos Develop a generic solution to automatically make sense of medical data Extract semantics from medical data Structure medical knowledge in order to use it in epidemiological studies or in decision support Give medical users the possibility of writing their own expert rules (with no linguistic knowledge) Evaluation on two different application domains in order to ensure the solution is generic: hospital acquired infections and cancer 3

4 Example Le patient a présenté par la suite un syndrome septique avec hyperthermie le [T-5J]. [ ] drainage chirurgical le [T-4J] [...] Une antibiothérapie empirique avait été initiée par l'association Augmentin, Gentamycine et Flagyl : Un Staphylococcus aureus a été isolé dans les prélèvements bactériologiques peropératoires. Level of importance Risk # n Micro- Organism Risk pattern Treatment The postoperative consequences were marked by abdominal pain and fever, associated with a hyperleucocytosis and inflammation. It was due to multiple intra-peritoneal abscesses that required a peritoneal toilet on September Bacteria Symptoms Antibiotic 29th. It was an infection with Klebsiella only sensitive to Tienam Ontologie I.N. Staphylococcus aureus syndrome septique avec hypertherm ie Augmentin, Gentamyci ne et Flagyl 4

5 Synodos Refine linguistic processing of temporal expressions Interface between linguistic analysis and multiple medical terminologies Interface between linguistic analysis and knowledge representation (transition rules) Independency of expert rules and linguistic rules Integrate the different processing modules 5

6 6 Linguistic analysis: terminologies, syntax, semantics & temporal expressions

7 Anonymisation The first step consists in anonymising all EMRs 7

8 8 Multi-terminology server

9 Linguistic Analysis The next step consists in analysing the EMR with linguistic technologies in order to extract as much «meaning» as possible from the sentences contained in individual records. Incremental processing with HOLMES NLP platform (integration of statistical & machine learning methods with symbolic methods) and medical terminology (via "Portail terminologiqe de santé" (CiSMeF)) Processing stages of Hybrid Operable platform for Language Management and Extensible Semantics (HOLMES) Tokenisation Lemmatisation and morphological analysis Part-of-speech tagging Syntactic dependency parsing Semantic parsing All information obtained at a given stage remains available as input for any further processing module 9

10 Linguistic Analysis: Syntactic Dependency Parsing French probabilistic parser based on the Malt Parser Outputs "shallow" dependencies converted to "deep" with in-house declarative graph transformation language based on Attributive Graph Grammars (Taentzer, 2000) Correction & conversion to Stanford Dependency schema for French (Robin, et al., submitted to LREC2014) 10

11 Syntactic Dependency Parsing: an example Nous avons découvert un abcès pulmonaire chez le patient en (We discovered a lung abscess in this patient in 2001.) Token Lemma POS Nous nous CLS avons avoir V découvert découvrir VPP un un DET abcès abcès NC pulmonaire pulmonaire ADJ chez chez P le le DET patient patient NC en en P NC 11.. PONCT

12 Syntactic Dependency Parsing : an example Syntactic parsing yields a graph structure of syntactic dependencies 12

13 Linguistic Analysis: Semantic Parsing Detection of events (verbal, nominal adjectival) Detection of medical terms via terminology (CiSMeF) Detection of temporal expressions via TokensRegex (Stanford) local grammars Detection of relations Set of relations is extensible/modifiable Relations currently used (29): "Traditional" semantic roles (10): AGENT, THEME, BENEFICIARY, EXPERIENCER, etc. Logical relations (3): AND, OR, IMPLIES Temporal relations «à la ISO-TimeML» (6): DURING, BEFORE, AFTER, etc. Domain-specific (7): DOSAGE, LEVEL, CONSUMPTION, PERIODICITY, etc. Others (3): NEGATION, MODALITY, HAS_PROPERTY Lexical resources: VerbAction (Hathout et al., 2002) (deverbal event nouns), Lefff (Sagot, 2010) (verb subcategorisation), WoLF (extracted certain semantic classes), hand-coded gazetteers (first names, countries, time units, units of measurement, etc.) 13 Graph transformation grammar applied to syntactic dependency graphs semantic relations

14 Semantic Parsing : an example Semantic parsing yields a graph structure of semantic dependencies built on both the syntactic dependency graph and access to the medical terminology Terminology server 14

15 Limitations of Linguistic Analysis Purely linguistic processing is limited to the sentence level. As a consequence, cross-sentence and document-level knowledge is not accessible at this level. Linguistic processing does not encode ontological knowledge, which requires inference. Knowledge representation and web semantic technologies facilitate access to information across an entire document, or set of documents and provides reasoning facilities in order to render implicit information explicit/accessible. 15

16 From Semantic Knowledge to Ontological Knowledge: an example Linguistic server Transition rule Expert server Semantic graph Ontological graph 16

17 17 Semantic Web processing: facts and ontological graphs

18 18 What type of knowledge do we want to model?

19 How to represent knowledge? Relational model? No semantic between elements No expressive power RDF (Resource Description Framework) model? Graph representation Subject predicate object triples To model individuals Does not define the semantics of the application domain Very limited expressive power RDFS (Resource Description Framework Schema) model? Defines the semantic of the application domain Concepts, properties, hierarchy of properties, instances Limited expressive power 19 => OWL (Web Ontology Language) model Specification of ontologies Description Logics Cardinalities A patient must have a unique identifier (i.e. «22122») More expressive language

20 How to populate the knowledge base with a OWL representation model? Semantic Facts?? Transition Rules Add information after linguistic processing Linguistic processing says lung abscess was discovered in 2001, but doesn t say it is an antecedent Converts the linguistic data into facts (populating the knowledge base) 20 While transition rules are mainly based on linguistic information they also encode some expert knowledge.

21 Linguistic-based Transition Rules To convert information into explicit semantic relations hasdiagnostic hasdiagnosticdate «Nous avons découvert un abcès pulmonaire chez le patient en 2001» («We discovered a lung abscess in this patient in 2001») (m1-isa- DISEASE) m1-isa? Linguistic server (m1, abcès pulmonaire) (t1, 2001) (e1, découvert) (p1, patient) BENEFICIARY (p1, e1) THEME (e1, m1) TIME (t1, T-12A) DURING (e1, t1) If THEME(e1,m1) and DURING(e1,t1) Then (m1-hasdiagnosticdate-t1) If BENEFICIARY(e1,p1) and THEME(e1,m1) and (m1-isa-disease) Then (p1-hasdiagnostic-m1) Knowledge server (abcès pulmonaire, hasdiagnosticdate, T-12A) (patient, hasdiagnostic, abcès pulmonaire) 21

22 Expert-based Transition Rules To enrich information extracted by the linguistic Transition Rules Terminology server m1-isa? If THEME(e1,m1) and DURING(e1,t1) and t1 > d Then (m1-isa-antecedent) d = T Knowledge server (abcès pulmonaire, isa, Antecedent) Linguistic server (m1, abcès pulmonaire) (t1, 2001) (e1, découvert) THEME (e1, m1) TIME (t1, T-12A) DURING (e1, t1) If THEME(e1,m1) and DURING(e1,t1) and t1 > d and (m1-isa-disease) then (m1-isa-medicalantecedent) d = T For the same rule, if (m1-isa-surgical PROCEDURE) Then (m1-isa-surgicalantecedent) (abcès pulmonaire, isa, MedicalAntecedent) (abcès pulmonaire, isa, SurgicalAntecedent) 22

23 Conclusion Transition rules: Interface between terminology server, linguistic processing and knowledge representation Linguistic issues: recognition of temporal expressions (e.g. deictic frequency, anaphora), temporal relations between events Knowledge representation issue: decomposition of complex concepts into simple one based on information comoing from the terminology server Evaluation (to be done) All modules separately at the component level System-level: on 700 EMRs distinct from the ones used for development purposes 23

24 Thank you! 24 Page 24

25 More information at: This project is supported by a grant from Agence Nationale de la Recherche (program ANR TecSan 2012)

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