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Retrieval of Similar Electronic Health Records using UMLS Concept Graphs

Retrieval of Similar Electronic Health Records using UMLS Concept Graphs. Laura Plaza and Alberto Díaz Universidad Complutense de Madrid. Motivation. When facing complex and untypical cases, physicians need to refer to similar previous cases

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Retrieval of Similar Electronic Health Records using UMLS Concept Graphs

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  1. Retrieval of Similar ElectronicHealth Records using UMLS Concept Graphs Laura Plaza and Alberto Díaz Universidad Complutense de Madrid

  2. Motivation • Whenfacingcomplex and untypical cases, physiciansneedtoreferto similar previous cases • Theadoption EHR by office-basedphysicians and hospitalsisincreasing • Butstillthe time requiredtofindthem can beprohibitiveif no effectiveaccessisprovided Given a reference record, retrieveothersfromtheclinicaldatabasethat are similar tothereferenceone Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  3. A Different IR Task • A mix of highlystructuredinformation + idiosyncraticnarrativetext • Uniquesublanguagecharacteristics: • Verblesssentences, punctuation, spellingerrors. • Synonyms and homonyms • Neologisms • Acronyms and abbreviations • Whentwo HR can beconsidered as similar? Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  4. Two EHR are Similar if… • Same symptom or sign (e.g. fever or 5 kg weight loss) • Same diagnosis (e.g. bacterial pneumonia) • Same test or procedure (e.g. cerebral NMR or endoscopy biopsy) • Same medicament (e.g. clopidogrel) • But … absent criteria are not relevant for the task!!! Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  5. Using UMLS for Concept Annotation • UMLS consists of three main components: the Specialist Lexicon, the Metathesaurus and the Semantic Network • We use MetaMap to translate free-form text to Metathesaurus concepts • Advantages: • Broad coverage • Performs word sense disambiguation • Numerous entries for acronyms and abbreviations • Etc. Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  6. OurProposal • A four-stepgraph-based method : • Extraction of UMLS concepts • Negationdetection • Semanticgraph-basedrepresentation • Ranking similar EHR CLINICAL HISTORY: Eleven yearsoldwith ALL, bonenarrowtransplanton Jan.2, nowwith 3 dayhistory of cough. IMPRESSION: No focal pneumonia. Likelychronicchanges at theleftlung base. Mild anterior wedging of thethoracic vertebral bodies. Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  7. OurProposal: Extracting UMLS Concepts • We use MetaMap toextractthe UMLS conceptsfromthe Metathesaurus and theirsemantictypesfromtheSemantic Network • But, accordingtotheexpert, notallconcepts are relevanttothetask • Thus, theexpertmappedthesecriteriatosemantictypes and onlyconceptsfromthosetypes are considered Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  8. OurProposal: Extracting UMLS Concepts Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  9. OurProposal: NegationDetection • Accordingtotheexpert, absentornegatedcriteria (e.g.Onadmission, thepatienthad no internalbleeding) are notrelevantforthetask • Thus, negated UMLS concepts are ignored • Negations in medical records usuallyappears in a reducednumber of forms, easytoidentifyusing a simple lexical scanner from regular expressions Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  10. OurProposal: NegationDetection Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  11. OurProposal: SemanticGraphRepresentation • First, the concepts are retrieved from the UMLS Metathesaurus along with their complete hierarchy of hypernyms (is-a relations). • Second, all concept hierarchies for each category are merged, building a unique graph for each category in the EHR • Finally, each concept is assigned a weight, using the Jaccard similarity coefficient, attaching greater importance to specific concepts than to general ones Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  12. OurProposal: SemanticGraphRepresentation 1/5 2/5 3/5 4/5 5/5 Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  13. OurProposal: Ranking Similar EHR • We compute thesimilarityamongthereference EHR and all records in thedatabase, and rankthem • Given two graphs, A and B, so that the similarity of A to B has to be measured: • First, each concept of A which is not in B assigns a score equal to 0, while each concept of A which is also in B assigns a score equal to its weight in the graph A • Next, the sum of the scores for all concepts in A is computed. • Finally, this result is normalized in the interval [0, maximum similarity]. Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  14. OurProposal: Ranking Similar EHR Graph A Graph B Clinical finding Clinical finding 1/11 Finding by site Finding by site Disease 2/11 Respiratory Disorder by Disorder by finding body site Infectious 3/5 body site 3/11 disease ... Functional finding ... of respiratory tract 8/11 4/5 Virus Diseases Bacterial Bacterial Coughing pneumonia pneumonia 5/5 9/11 Pneumonia due to Pneumonia due anaerobic bacteria to Streptococcus 10/11 Pneumococcal Pneumonia due pneumonia to pleuropneumonia 11/11 Mycoplasma pneumonia Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  15. Experiments • Test collection: 50 radiologyreportsfromtheCMC-NLP 2007 Challenge corpus • Query collection: a subset of 20 reports from the test collection • Two hospital physicians were asked to select, for each report in the query collection, the most similar reports within the test collection • There is a substantial agreement between judges (Kappa test, k=0.7980) • Precision and Recall of our method are compared with those obtained by a term-based approach Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  16. Results Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

  17. Conclusion and FutureWork • The method achieves relatively high precision and recall which are also well balanced • UMLS occasionally fails to recover relevant concepts especially when expressed in their shortened forms • Another impairment to concept identification comes from the spelling errors in the clinical records • Future work will test the method on a different evaluation collection which will present longer medical records structured in different sections Retrieval of Similar ElectronicHealth Records Using UMLS Concept Graphs (Plaza and Díaz, 2010)

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