Value of Scholarship
Up to $30,000
| Location |
University of Western Sydney, Graduate Research School |
| Discipline |
- Maths & Computing > Bioinformatics
- Maths & Computing > Computer Science
|
| App. deadline |
Applications accepted year round/until filled |
| Funding |
|
| Project ID |
020 |
| Eligibility |
Australian residents only |
There are a number of Clinical Classification & Coding andTerminology systems that are developed as international standards andadopted by various countries including Australia.
These include (but not limited to):
- InternationalClassification of Diseases (ICD),
- InternationalClassification of Functioning, Disability and Health(ICF)
- International Classification of Health Interventions(ICHI) developed and maintained by WHO-World Health Organisation,and
- Systematized Nomenclature of Medicine-ClinicalTerminology (SNOMED-CT) developed and maintained by IHTSDO-InternationalHealth Terminology Standards Development Organisation.
Theseclassification and terminology systems are developed independently to eachother and expected to work in harmony within clinical software. However dueto lack of harmonisation between these clinical terminology andclassification systems, there is much human involvement required intranslating from one to another. Such human involvement createsinconsistencies and errors in coding.
With wider usage ofsoftware for management of health data, there is an urgent need for havingcomputer-assisted harmonisation between these classification andterminology systems. Further these are evolving ontologies. Therefore,there is a greater need for keeping the ontological continuity as the coreclassification or terminological systems evolve.
This researchis focused on involving machine learning techniques for the purposeextracting relevant ICD, ICHI and ICF codes, based on SNOMED-CT terms foundin free text data that relates to a particular episode of care. Also otherinformation such as
- previous mapping of similar cases toICD, ICHI or ICF codes
- other non-clinical (age, sex) and historicaldata (chronic diabetes) relevant to the episode ofcare
- clinician or facility where patient was treated, etc., would require to be considered in improving the accuracy thealgorithm.
What does the Scholarship provide?
- Domestic students receive a tax-freestipend of up to $30,000 per annum for up to 3 years, and a funded place inthe doctoral program.
- International students receive a tax-free stipend up to $30,000 per annum. Outstanding students may be awarded aTuition Fee Waiver valued at approximately $24,000 per annum for up to 3years.
- International students receive up to $3,267 towards the costof an Overseas
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