The Australian Genomics Mitochondrial Flagship: A national program delivering mitochondrial diagnoses.

Purpose: Families living with mitochondrial diseases (MD) often endure prolonged diagnostic journeys and invasive testing, yet many remain without a molecular diagnosis. The Australian Genomics Mitochondrial Flagship, comprising clinicians, diagnostic, and research scientists, conducted a prospective national study to identify the diagnostic utility of singleton genomic sequencing using blood samples.

Methods: A total of 140 children and adults living with suspected MD were recruited using modified Nijmegen criteria (MNC) and randomized to either exome + mitochondrial DNA (mtDNA) sequencing or genome sequencing.

Semantic Search for Large Scale Clinical Ontologies.

Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies. A search algorithm that overcomes this problem is useful in applications such as concept normalisation and ontology matching, where concepts can be referred to in different ways, using different synonyms. In this paper, we present a deep learning based approach to build a semantic search system for large clinical ontologies.

Can Wikipedia Be Used to Derive an Open Clinical Terminology?

Clinical terminologies play an essential role in enabling semantic interoperability between medical records. However, existing terminologies have several issues that impact data quality, such as content gaps and slow updates. In this study we explore the suitability of existing, community-driven resources, specifically Wikipedia, as a potential source to bootstrap an open clinical terminology, in terms of content coverage.

Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.

Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, especially for a wide range of complex and less prevalent clinical cases, remains an open research problem. This study investigates this problem on a broad spectrum of diagnostic codes and, in particular, investigates the effectiveness of utilising SNOMED CT for ICD-10 diagnosis coding.

Automatic classification of diseases from free-text death certificates for real-time surveillance.

Background: Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV.

Cadec: A corpus of adverse drug event annotations.

CSIRO Adverse Drug Event Corpus (Cadec) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.

A method for the semantic enrichment of clinical trial data.

Clinical trial data have historically been implemented using relational databases. While this has expedited the dissemination of data among partners, it has hindered on the ability to swiftly query the data by relying on monolithic tables. This paper outlines a project that investigates the semantic enrichment of a large-scale longitudinal clinical trial, the AIBL study, by reusing entities from existing ontologies.

Developing a national emergency department data reference set based on SNOMED CT.

Emergency departments around Australia use a range of software to capture data on patients' reason for encounter, presenting problem and diagnosis. The data collected are mainly based on descriptions and codes of the International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM), with each emergency department having a tailored list of terms. The National E-Health Transition Authority is introducing a standard clinical terminology, the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT), as one of the building blocks of an e-health infrastructure in Australia.

Coding of thoughts, words and things.

This short paper highlights some new work being performed at the National Centre for Classification in Health (NCCH), relating newly created term sets developed for specific purposes to existing reference terminologies and classifications such as SNOMED CT and ICD-10-AM. It describes some of the inherent difficulties experienced by the NCCH team in interpreting terms in the term set and therefore in locating equivalent concepts in reference terminologies and classifications, in the absence of a context with which to associate each term. Also examined is the effect that a person's background and past experience has on their understanding and interpretation of clinical terms and how this results in inconsistent "world views".