automatic ontology oriented clinical concept extraction from free-text reports for csi

7/28/2019 Automatic Ontology Oriented Clinical Concept Extraction From Free-text Reports for CSI

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Automatic ontology oriented clinicalconcept extraction from free-textreports for CSI (Computer Semantic Interoperability)


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Presenters

David Mendes (PhD Student at Universidade de vora)

Irene Rodrigues

Departamento de Informtica daUniversidade de voraCENTRIA Centre for Artificial Intelligence of UNL


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Agenda

The research activities framed

Issues faced

Proposal Availability of tools & techniques/technologies

Conclusions

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Background

Problem Faced Clinical Practice ontologiesdont exist so far with appropriate

characteristics for adequate reasoning and alignment with Well FoundedStandards

Automatic acquisition (population) is compulsory given that the size ofthe available data residing in EHRs renders manual curating impossible

Conclusions Automatic clinical concept acquisition tools have come of age to enable

the automatic population of a suitable ontology for Clinical Practice

The characteristics of the target Ontology is a matter not handled in thepresent work but is a research line issue by itself.

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CPR vs OGMS as target

Both Computer Based Patient Record ontology (CPR) andOntology for General Medical Science (OGMS) are ontologies ofentities existing during clinical encounters.

Include very general terms that are used across medicaldisciplines, including: 'disease', 'disorder', 'disease course',

symptom, 'diagnosis', 'patient', and 'healthcare provider'. Both use the Basic Formal Ontology (BFO) as an upper-level

ontology as support for Ontological Realism. Both provide a formal theory of disease that can be further

elaborated by specific disease ontologies. This theory isimplemented using OWL-DL.

CPR is W3C Standard since 2009. OGMS is still very seminal .

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http://www.ifomis.org/bfo/http://www.ifomis.org/bfo/


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Ontology population

Subjective, the symptoms section S where we extract directlyinto a cpr:symptom

Objective, the O section where are sign records that we takeas generator for cpr:clinical-findings orcpr:sign-finding

Assessment, the analysis section A which are the clinicalinvestigation acts cpr:clinical-investigation-act Can be clinical-analisys-act, diagnostic-procedure or laboratory-test

Plan, the P section where the cpr:therapeutic-acts can beextracted Can be medical-therapy, physical-therapy, psychological-therapy or

therapeutic-procedure

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Acquisition Flowchart

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CSI with QA

Our team is two-headed: Knowledge representation and acquisition team

in vora

Discourse controller team for smart Question-Answering in Coimbra

Currently only controlled cardiologyenvironment is being developed. Generalizationbeing the most challenging issue.

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Current on-going controlled results

Q: What is the patients personal history?A: Hypertension for 15 years; Diabetes Mellitus type 2 for

10 years; Cholecystectomy 2 years ago; Diabetic father;Obese BMI 26,5; Abdominal perimeter 106 cm.

Q: What is the suggested diagnosis?A: Labratory routines: lipid profile; HgA1c; Rx thorax; ECG inrest; Ecochardiogram; Effort test (Effort proof or Chardiacscintigraphy);

Q: What is the immediate recommended therapy assuming

that AHT and Diabetes are not controlled ?A: Rich fiber and vegetable diet; polifraccionate and

hiposaline; IECA or ARA II; Calcium Antagonist;Metformine; Estatine;

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Conclusion

Automatic clinical concept acquisition toolshave come of age to enable the automaticpopulation of a suitable ontology for Clinical

Practice The development of an Ontology based layer

to represent the acquired medical

knowledge allows for CSI at a conceptuallevel

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Thank you very much !!

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automatic ontology oriented clinical concept extraction from free-text reports for csi

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