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Mining Electronic Health Records for Insights

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This is the slide deck from the October 15, 2015 presentation "Mining Electronic Health Records for Insights: Beyond Ontology Based Text Mining"

Technologie
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Mining Electronic Health Records Go Beyond Ontology Based Text Mining October 15th 2015 Mining Electronic Health Records #110/16/2015
• Information management company providing text analysis, data management and state-of-the-art semantic technology • 70 software developers in Sofia, Bulgaria • Presence in London and New York • Clients include BBC, FT, AstraZeneca, DoD, Wiley & Sons • Over 400 person-years in R&D to create a one-stop shop for: – Content enrichment – Data management – Graph database engine Ontotext Mining Electronic Health Records #210/16/2015
Technology Portfolio Mining Electronic Health Records #310/16/2015
Mining Electronic Health Records #410/16/2015 Clients
Healthcare Insights Mining Electronic Health Records #510/16/2015
Mining Electronic Health Records #610/16/2015 • An ontology models discrete knowledge domain • All ontology concepts have a definition • All ontology concepts have alternative labels • Where appropriate, ontology concepts have additional labels • Inference can be applied Chronic Obstructive Pulmonary Disease rdf:typeCOPD Disease skos:prefLabel skos:altLabel COLD Shortness of Breath rdf:type Symptom hasSymptom skos:altLabel Chronic Airflow Obstruction rdf:type Disease Respiratory Disease Ontology Based IE
Ontology Based IE - problems Mining Electronic Health Records #710/16/2015 • Does not model a domain completely (both on instance level and labels)  Extend ontologies  Ontology enrichment via instance mappings • Labels contain additional qualifying information  Definition of literals rewrite and ignore rules • Labels does not reflect natural language  Apply “flexible” gazetteers • Ambiguity in terminology  Pre-filtering  Ranking  Semantic instance mappings
Vocabulary Enrichment – Semantic Mappings Mining Electronic Health Records #810/16/2015 Chronic obstructive airway disease NOS Chronic obstructive lung disease NOS Chronic obstructive pulmonary disease, unspecified Chronic obstructive lung disease Chronic obstructive airways disease NOS Chronic obstructive lung disease (disorder) CAFL - Chronic airflow limitation Chronic irreversible airway obstruction ICD 10 CM SNOMED CT US skos:closeMatch
Ontology Based IE - problems Mining Electronic Health Records #910/16/2015 • Does not model a domain completely (both on instance level and labels)  Extend ontologies  Ontology enrichment via instance mappings • Labels contain additional qualifying information  Definition of literals rewrite and ignore rules • Labels does not reflect natural language  Apply “flexible” gazetteers • Ambiguity in terminology  Pre-filtering  Ranking  Semantic instance mappings
Vocabulary Enrichment – Synonym Enrichment Mining Electronic Health Records #1010/16/2015 Tumor Tumour Abdomen Abd Tumor of abdomen Tumor of abd Tumour of abdomen Tumour of abd
Ontology Based IE - problems Mining Electronic Health Records #1110/16/2015 • Does not model a domain completely (both on instance level and labels)  Extend ontologies  Ontology enrichment via instance mappings • Labels contain additional qualifying information  Definition of literals rewrite and ignore rules • Labels does not reflect natural language  Apply “flexible” gazetteers • Ambiguity in terminology  Pre-filtering  Ranking  Semantic instance mappings
Ontology Based IE – example Mining Electronic Health Records #1210/16/2015
Flexible Gazetteers Mining Electronic Health Records #1310/16/2015 • Pre-coordinated terms cannot match all natural language terms, especially those used in narrative medical text!  Inversions concept “knee injury” vs. “injury of knee” in text  Gaps due to additional qualifiers concept “periorbital swelling” vs. “periorbital soft tissue swelling” in text
Detection of negations Mining Electronic Health Records #1410/16/2015 • The ability to reliably identify negated medical statements in text may significantly affect the quality of the extracted information.  Adverbial Negation  Negations in noun phrase  Prepositional Negation  Adjective Negation  Verb Negation
Temporality Identification Mining Electronic Health Records #1510/16/2015 • Temporal resolution for events in clinical notes is crucial for an accurate definition of patient history, current medical condition and assigned treatment. • Identified temporality classes are:  Historical  Hypothetical (“Not particular”)  Recent • The temporality data is important to be normalized based on the medical documents meta data (date of report/visit)!
Temporality Identification - Example Mining Electronic Health Records #1610/16/2015
Post-coordination Patterns Mining Electronic Health Records #1710/16/2015 • It is impossible to fully describe medical knowledge in term of fully qualified concepts! • Natural language does not follow the standardized descriptions defined by domain ontologies! • Concepts must describe basic entities • Entity properties can be described by different qualifier classes • Patterns can generate new concepts, combining specific instance and qualifier classes
Post-coordination Patterns - Examples Mining Electronic Health Records #1810/16/2015 • Example pattern: <disease> or <morphologic abnormality> as right most concept in a noun phrase, preceded by <qualifier> and <body structure>
Data Modeling Mining Electronic Health Records #1910/16/2015 • Based on normalized data • … but allowing extension with free text • Allow data fusion with background knowledge • Capture all aspects of the extracted information • Tightly coupled with the context • Provide provenance and confidence score • Explorable! Not just searchable
Data provenance: graph <http://linkedlifedata.com/resource/document/CD8672> Data Modeling Mining Electronic Health Records #2010/16/2015 rdf:typePatient XYZ Patient male hasGender hasBirthDate 1956/09/20 xsd:date hasDiagnose http://linkedlifedata.com/resource/icd9cm/157.9 current Disease hasStatus skos:prefLabel Malignant neoplasm of pancreas rdf:type Data provenance: graph <http://linkedlifedata.com/resource/document/CN127753> hasTreatment http://linkedlifedata.com/resource/treatment/DT127753 Treatment hasDrug hasDosage rdf:type http://linkedlifedata.com/resource/drug/irinotecan 180 mg/ 1 m2 for 80 min
Data provenance: graph <http://linkedlifedata.com/resource/drugBroshure/CAMPTOSAR> Maximum Daily Dosage Data Modeling – KB Mining Electronic Health Records #2110/16/2015 http://linkedlifedata.com/resource/drugDosage/DD127753 Dosage hasMedication hasPopulationGroup rdf:type http://linkedlifedata.com/resource/drug/irinotecan Adult hasAdministration Route http://linkedlifedata.com/resource/route/subcutaneus hasAdministration Form http://linkedlifedata.com/resource/form/injection http://linkedlifedata.com/resource/icd9cm/157.9 hasIndication hasDosageValue 180 hasDosageUnit mg hasDenominatorValue 1 hasDenominatorUnit m2
Semantic Data Exploration and Mining Mining Electronic Health Records #2210/16/2015 • Build Linked Data out of extracted facts and background knowledge • Semantic Faceted Search • Cross Entity Search & Exploration • Expert Text Mining Search in pre-annotated documents  Combine semantic annotations with PoS elements  Identify post-coordination patterns  Identify relations patterns  Query expansion using background knowledge
• Information Extraction from EHRs is still a challenge! • Making use of the extracted data is even more challenging  • Ontotext provides the technology stack to make it work! life-sciences@ontotext.com Thank you! Mining Electronic Health Records #2310/16/2015

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Mining Electronic Health Records for Insights

  • 1. Mining Electronic Health Records Go Beyond Ontology Based Text Mining October 15th 2015 Mining Electronic Health Records #110/16/2015
  • 2. • Information management company providing text analysis, data management and state-of-the-art semantic technology • 70 software developers in Sofia, Bulgaria • Presence in London and New York • Clients include BBC, FT, AstraZeneca, DoD, Wiley & Sons • Over 400 person-years in R&D to create a one-stop shop for: – Content enrichment – Data management – Graph database engine Ontotext Mining Electronic Health Records #210/16/2015
  • 3. Technology Portfolio Mining Electronic Health Records #310/16/2015
  • 4. Mining Electronic Health Records #410/16/2015 Clients
  • 5. Healthcare Insights Mining Electronic Health Records #510/16/2015
  • 6. Mining Electronic Health Records #610/16/2015 • An ontology models discrete knowledge domain • All ontology concepts have a definition • All ontology concepts have alternative labels • Where appropriate, ontology concepts have additional labels • Inference can be applied Chronic Obstructive Pulmonary Disease rdf:typeCOPD Disease skos:prefLabel skos:altLabel COLD Shortness of Breath rdf:type Symptom hasSymptom skos:altLabel Chronic Airflow Obstruction rdf:type Disease Respiratory Disease Ontology Based IE
  • 7. Ontology Based IE - problems Mining Electronic Health Records #710/16/2015 • Does not model a domain completely (both on instance level and labels)  Extend ontologies  Ontology enrichment via instance mappings • Labels contain additional qualifying information  Definition of literals rewrite and ignore rules • Labels does not reflect natural language  Apply “flexible” gazetteers • Ambiguity in terminology  Pre-filtering  Ranking  Semantic instance mappings
  • 8. Vocabulary Enrichment – Semantic Mappings Mining Electronic Health Records #810/16/2015 Chronic obstructive airway disease NOS Chronic obstructive lung disease NOS Chronic obstructive pulmonary disease, unspecified Chronic obstructive lung disease Chronic obstructive airways disease NOS Chronic obstructive lung disease (disorder) CAFL - Chronic airflow limitation Chronic irreversible airway obstruction ICD 10 CM SNOMED CT US skos:closeMatch
  • 9. Ontology Based IE - problems Mining Electronic Health Records #910/16/2015 • Does not model a domain completely (both on instance level and labels)  Extend ontologies  Ontology enrichment via instance mappings • Labels contain additional qualifying information  Definition of literals rewrite and ignore rules • Labels does not reflect natural language  Apply “flexible” gazetteers • Ambiguity in terminology  Pre-filtering  Ranking  Semantic instance mappings
  • 10. Vocabulary Enrichment – Synonym Enrichment Mining Electronic Health Records #1010/16/2015 Tumor Tumour Abdomen Abd Tumor of abdomen Tumor of abd Tumour of abdomen Tumour of abd
  • 11. Ontology Based IE - problems Mining Electronic Health Records #1110/16/2015 • Does not model a domain completely (both on instance level and labels)  Extend ontologies  Ontology enrichment via instance mappings • Labels contain additional qualifying information  Definition of literals rewrite and ignore rules • Labels does not reflect natural language  Apply “flexible” gazetteers • Ambiguity in terminology  Pre-filtering  Ranking  Semantic instance mappings
  • 12. Ontology Based IE – example Mining Electronic Health Records #1210/16/2015
  • 13. Flexible Gazetteers Mining Electronic Health Records #1310/16/2015 • Pre-coordinated terms cannot match all natural language terms, especially those used in narrative medical text!  Inversions concept “knee injury” vs. “injury of knee” in text  Gaps due to additional qualifiers concept “periorbital swelling” vs. “periorbital soft tissue swelling” in text
  • 14. Detection of negations Mining Electronic Health Records #1410/16/2015 • The ability to reliably identify negated medical statements in text may significantly affect the quality of the extracted information.  Adverbial Negation  Negations in noun phrase  Prepositional Negation  Adjective Negation  Verb Negation
  • 15. Temporality Identification Mining Electronic Health Records #1510/16/2015 • Temporal resolution for events in clinical notes is crucial for an accurate definition of patient history, current medical condition and assigned treatment. • Identified temporality classes are:  Historical  Hypothetical (“Not particular”)  Recent • The temporality data is important to be normalized based on the medical documents meta data (date of report/visit)!
  • 16. Temporality Identification - Example Mining Electronic Health Records #1610/16/2015
  • 17. Post-coordination Patterns Mining Electronic Health Records #1710/16/2015 • It is impossible to fully describe medical knowledge in term of fully qualified concepts! • Natural language does not follow the standardized descriptions defined by domain ontologies! • Concepts must describe basic entities • Entity properties can be described by different qualifier classes • Patterns can generate new concepts, combining specific instance and qualifier classes
  • 18. Post-coordination Patterns - Examples Mining Electronic Health Records #1810/16/2015 • Example pattern: <disease> or <morphologic abnormality> as right most concept in a noun phrase, preceded by <qualifier> and <body structure>
  • 19. Data Modeling Mining Electronic Health Records #1910/16/2015 • Based on normalized data • … but allowing extension with free text • Allow data fusion with background knowledge • Capture all aspects of the extracted information • Tightly coupled with the context • Provide provenance and confidence score • Explorable! Not just searchable
  • 20. Data provenance: graph <http://linkedlifedata.com/resource/document/CD8672> Data Modeling Mining Electronic Health Records #2010/16/2015 rdf:typePatient XYZ Patient male hasGender hasBirthDate 1956/09/20 xsd:date hasDiagnose http://linkedlifedata.com/resource/icd9cm/157.9 current Disease hasStatus skos:prefLabel Malignant neoplasm of pancreas rdf:type Data provenance: graph <http://linkedlifedata.com/resource/document/CN127753> hasTreatment http://linkedlifedata.com/resource/treatment/DT127753 Treatment hasDrug hasDosage rdf:type http://linkedlifedata.com/resource/drug/irinotecan 180 mg/ 1 m2 for 80 min
  • 21. Data provenance: graph <http://linkedlifedata.com/resource/drugBroshure/CAMPTOSAR> Maximum Daily Dosage Data Modeling – KB Mining Electronic Health Records #2110/16/2015 http://linkedlifedata.com/resource/drugDosage/DD127753 Dosage hasMedication hasPopulationGroup rdf:type http://linkedlifedata.com/resource/drug/irinotecan Adult hasAdministration Route http://linkedlifedata.com/resource/route/subcutaneus hasAdministration Form http://linkedlifedata.com/resource/form/injection http://linkedlifedata.com/resource/icd9cm/157.9 hasIndication hasDosageValue 180 hasDosageUnit mg hasDenominatorValue 1 hasDenominatorUnit m2
  • 22. Semantic Data Exploration and Mining Mining Electronic Health Records #2210/16/2015 • Build Linked Data out of extracted facts and background knowledge • Semantic Faceted Search • Cross Entity Search & Exploration • Expert Text Mining Search in pre-annotated documents  Combine semantic annotations with PoS elements  Identify post-coordination patterns  Identify relations patterns  Query expansion using background knowledge
  • 23. • Information Extraction from EHRs is still a challenge! • Making use of the extracted data is even more challenging  • Ontotext provides the technology stack to make it work! life-sciences@ontotext.com Thank you! Mining Electronic Health Records #2310/16/2015