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1.  Towards semantic search and inference in electronic medical records: An approach using concept-­based information retrieval 
The Australasian Medical Journal  2012;5(9):482-488.
Background
This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching.
Aim
The concept-based approach is intended to overcome specific challenges we identified in searching medical records.
Method
Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology.
Results
Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision.
Conclusion
The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.
doi:10.4066/AMJ.2012.1362
PMCID: PMC3477777  PMID: 23115582
Electronic medical records; Information retrieval; Semantic search and inference; Health informatics.
2.  Symbolic rule-based classification of lung cancer stages from free-text pathology reports 
Objective
To classify automatically lung tumor–node–metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification.
Design
By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine–clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage.
Measurements
The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines.
Results
Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches.
Conclusion
A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.
doi:10.1136/jamia.2010.003707
PMCID: PMC2995652  PMID: 20595312
3.  Identifying Symptom Groups from Emergency Department Presenting Complaint Free Text using SNOMED CT 
AMIA Annual Symposium Proceedings  2011;2011:1446-1453.
Patients presenting to Emergency Departments may be categorised into different symptom groups for the purpose of research and quality improvement. The grouping is challenging due to the variability in the way presenting complaints are recorded by clinical staff. This work proposes analysis of the presenting complaint free-text using the semantics encoded in the SNOMED CT ontology. This work demonstrates a validated prototype system that can classify unstructured free-text narratives into patient’s symptom group. A rule-based mechanism was developed using variety of keywords to identify the patient’s symptom group. The system was validated against the manual identification of the symptom groups by two expert clinical research nurses on 794 patient presentations from six participating hospitals. The comparison of system results with one clinical research nurse showed 99.3% sensitivity; 80.0% specificity and 0.9 F-score for identifying “chest pain” symptom group.
PMCID: PMC3243271  PMID: 22195208

Results 1-3 (3)