A Simple Pipeline Application for Identifying and Negating SNOMED CT in Free Text

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Transcription:

A Simple Pipeline Application for Identifying and Negating SNOMED CT in Free Text Anthony Nguyen 1, Michael Lawley 1, David Hansen 1, Shoni Colquist 2 1 The Australian e-health Research Centre, CSIRO ICT Centre, Brisbane, Australia 2 Queensland Cancer Control Analysis Team, Queensland Health, Brisbane, Australia

Medical Free Text to SNOMED CT Mapping Medical free text Non-standardised Individual & institutional variation Manual extraction of information SNOMED CT Standard reference terminology 1 Large formal ontology Aggregate clinical information for retrieval & analysis 1 SNOMED CT is identified by NeHTA as the standard ontology to be used in systems within Australian Healthcare Free-text Report Detect Negation Phrases Parse into Medical Terms Figure 1. General methodology for coded biomedical terminology mapping from medical free text

Case Study: Cancer Notifications Manual review of (text based) medical records Figure 2. De-identified pathology report demonstrating how cancer notifications was historically performed Multi-year cancer information reporting delay Expensive and labour intensive process Subject to omission errors Cases inadvertently skipped / keywords missed

Goal Hypothesis: Automated computer system could automatically perform the time and labour intensive manual coding of clinical information Method: Automatically scan free-text medical documents for clinically relevant terms Metastatic Small Cell Carcinoma

Pipeline Application [Tools] Figure 3. Pipeline application for the annotation of SNOMED CT concepts from free text General Architecture for Text Engineering (GATE) Open source architecture for natural language processing (NLP) MetaMap Transfer (MMTx) application Identify concepts in free text which are restricted to the SNOMED CT source Negation detection algorithm (NegEx) Detection of negation phrases Ontology server (OntoServer) SNOMED CT semantics to identify subsets of findings and diseases concepts for negation Development corpus (AUSLAB) Lung cancer reports from state-wide pathology information system

Pipeline Application Document Reset: Restores the document to its original state by deleting existing annotations Tokeniser: Splits the document into: Tokens Length measurements & units TNM cancer stages Legacy SNOMED IDs De-identified information

Pipeline Application Sentence Splitter: Segment text into sentences using regular expressions UMLS (MMTx) Annotator: Map strings in free text to closest concepts in UMLS UMLS concepts were restricted to the SNOMED CT source

Pipeline Application UMLS to SNOMED CT Mapper: UMLS concepts mapped to SNOMED CT using UMLS Metathesaurus database file Only active concepts were retained SNOMED ID tokens mapped to SNOMED CT concept

Pipeline Application Negation Phrase (NegEx) Finder Finds common medical negation phrases SNOMED CT Negation (NegEx) Applicator Tags negated concepts by associating negation phrases with neighbouring SNOMED CT findings or disease concepts SNOMED CT sub-hierarchies considered for negation: 404684003 clinical finding (e.g. decreased capillary fragility, diabetes mellitus, dyspnea. ) 49755003 morphologically abnormal structure (e.g. wallerian degeneration, carcinoma ) 363787002 observable entity (e.g. status of invasion by tumour ) 272379006 event (e.g. exposure to toxin, accident caused by bench saw )

Results

Results

Symbolic Rule-Based Post-Coordination Subsumption relationships can be taken advantage of to retrieve concepts which relate to the same disease, anatomy or finding, or infer if two descriptions relate to the same concept. Example Lung Resection Subsumption Test Test if following post-coordinated expression template <procedure> : {260686004 method = <procedure.method>,405813007 procedure site - Direct = <topology> } is subsumed by: 119746007 lung excision CSIRO.

Symbolic Rule-Based Post-Coordination CSIRO. Figure 4. Synoptic factor extraction using SNOMED CT semantics

The Australian e-health Research Centre Michael Lawley Principal Research Scientist Phone: +61 7 3253 3609 Email: michael.lawley@csiro.au Web: http://aehrc.com Thank you Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: Enquiries@csiro.au Web: www.csiro.au

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