This document discusses an ontology proposal for clinical practice justification in natural language. It presents an ontology for general clinical practice (OGCP) that was developed using several foundational ontologies from the OBO Foundry. An acquisition flowchart is described for automatically populating the OGCP by extracting clinical concepts from electronic health records using natural language processing. The populated ontology can then be used to provide clinical justifications and recommendations to users in a controlled natural language format.

1. Ontology based clinical practice justification in Natural Language

2. Presenters
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David Mendes (PhD Student at Universidade de Évora)

Irene Rodrigues (PhD Advisor)
Departamento de Informática da Universidade de Évora
CENTRIA – Centre for Artificial Intelligence of UNL

3. Co-authors
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Carlos Fernandes Baeta

Unidade Local de Saúde do Norte Alentejano 3

4. Agenda
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The research activities
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Ontology Proposal
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Acquisition Flowchart
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Clinical Advising ?
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Example
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Conclusions 4

5. HCist 2012 Background
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Ontology Proposal
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OGCP* was introduced with appropriate characteristics for adequate reasoning and alignment with Well Founded Standards
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Automatic acquisition (population) is compulsory given that the size of the available data residing in EHRs renders manual curating impossible
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Knowledge Base Enrichment was developed
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Automatic clinical concept acquisition tools are used for the automatic population of OGCP
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The resulting KB is sound in the underlying foundation of disease theory due to the round trip, debug and repair enrichment method.
* Ontology for General Clinical Practice 5

6. OGCP as target
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Use the Basic Formal Ontology (BFO) as an upper-level ontology as support for Ontological Realism.
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Is built upon 10 ontologies aligned according to the OBO Foundry principles (RO,FMA, SO, VSO, DO, Time, CVDO, OGMS,CPR)
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Provides a formal theory of Cardio Vascular disease. 6

7. Acquisition Flowchart 7

8. Round trip debug and repair
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DC* usage of pragmatics for NLP acquiring from SOAP reports in ULSNA+
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Insertion of facts and automatic consistency validation using distributed Consequence Based reasoners (elk).
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If inconsistent, the fact is redrawn
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The facts are generated in CNL# fashion
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Inferred axioms are built by the DC and valid by nature
* Discourse Controller
+ Unidade Local de Saúde do Norte Alentejano
# Controlled Natural Language 8

9. Clinical Advising ?

The decision of what inferred knowledge is presented, with its justifications, to the user is a task handled by the DC using the developed pragmatics introduced by our Colleague Dora Melo. 9

10. CNL Example
Q: What is the patient´s 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: Laboratory routines: lipid profile; HgA1c; Rx thorax; ECG in rest; Echocardiogram; Effort test (Effort proof or Cardiac scintigraphy);
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; 10

11. Conclusions
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We developed a knowledge representation infrastructure enabling the usage of highly optimized distributed consequence based reasoners that are referred in literature only in 2011.
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It is imperative, for the broad acceptance of these tooling by the medical community, that the inferences are justified using CNL and adequate terminology. 11

12. Thank you very much !!
Questions ? 12