Ontology developed for automatic acquisition and QA from Controlled Natural Language medical reports

1. Ontology based clinical practice justification in Natural Language

2. Presenters

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

Carlos Fernandes Baeta

Unidade Local de Saúde do Norte Alentejano 3

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

5. HCist 2012 Background

Ontology Proposal

OGCP* was introduced with appropriate characteristics for adequate reasoning and alignment with Well Founded Standards

Automatic acquisition (population) is compulsory given that the size of the available data residing in EHRs renders manual curating impossible

Knowledge Base Enrichment was developed

Automatic clinical concept acquisition tools are used for the automatic population of OGCP

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

Use the Basic Formal Ontology (BFO) as an upper-level ontology as support for Ontological Realism.

Is built upon 10 ontologies aligned according to the OBO Foundry principles (RO,FMA, SO, VSO, DO, Time, CVDO, OGMS,CPR)

Provides a formal theory of Cardio Vascular disease. 6

7. Acquisition Flowchart 7

8. Round trip debug and repair

DC* usage of pragmatics for NLP acquiring from SOAP reports in ULSNA+

Insertion of facts and automatic consistency validation using distributed Consequence Based reasoners (elk).

If inconsistent, the fact is redrawn

The facts are generated in CNL# fashion

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

We developed a knowledge representation infrastructure enabling the usage of highly optimized distributed consequence based reasoners that are referred in literature only in 2011.

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