An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

What matters for people with brain cancer? Selecting clinical quality indicators for an Australian Brain Cancer Registry.

BACKGROUND: The goal of a clinical quality registry is to deliver immediate gains in survival and quality of life by delivering timely feedback to practitioners, thereby ensuring every patient receives the best existing treatment. We are developing an Australian Brain Cancer Registry (ABCR) to identify, describe, and measure the impact of the variation and gaps in brain cancer care from the time of diagnosis to the end of life. METHODS: To determine a set of clinical quality indicators (CQIs) for the ABCR, a database and internet search were used to identify relevant guidelines, which were then assessed for quality using the AGREE II Global Rating Scale. Potential indicators were extracted from 21 clinical guidelines, ranked using a modified Delphi process completed in 2 rounds by a panel of experts and other stakeholders, and refined by a multidisciplinary Working Group. RESULTS: Nineteen key quality reporting domains were chosen, specified by 57 CQIs detailing the specific inclusion and outcome characteristics to be reported. CONCLUSION: The selected CQIs will form the basis for the ABCR, provide a framework for achievable data collection, and specify best practices for patients and health care providers, with a view to improving care for brain cancer patients. To our knowledge, the systematic and comprehensive approach we have taken is a world first in selecting the reporting specifications for a brain cancer clinical registry.

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. We propose a Triplet-BERT model and a method that generates training data directly from the ontologies. The model is evaluated using five real benchmark data sets and the results show that our approach achieves high results on both free text to concept and concept to concept searching tasks, and outperforms all baseline methods.

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. In order to establish the extent of the coverage, a team of expert clinical terminologists manually mapped a clinically-relevant subset of SNOMED CT to Wikipedia articles. The results show that approximately 80% of the concepts are covered by Wikipedia. Most concepts that do not have a direct match in Wikipedia are composable from multiple articles. These findings are encouraging and suggest that it should be possible to bootstrap an open clinical terminology from Wikipedia.

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. Hospital progress notes were used to provide the narrative rich electronic patient records for the investigation. A natural language processing (NLP) approach using mappings between SNOMED CT and ICD-10-AM (Australian Modification) was used to guide the coding. The proposed approach achieved 54.1% sensitivity and 70.2% positive predictive value. Given the complexity of the task, this was encouraging given the simplicity of the approach and what was projected as possible from a manual diagnosis code validation study (76.3% sensitivity). The results show the potential for advanced NLP-based approaches that leverage SNOMED CT to ICD-10 mapping for hospital in-patient coding.

SnoMAP: Pioneering the Path for Clinical Coding to Improve Patient Care.

The increasing demand for healthcare and the static resources available necessitate data driven improvements in healthcare at large scale. The SnoMAP tool was rapidly developed to provide an automated solution that transforms and maps clinician-entered data to provide data which is fit for both administrative and clinical purposes. Accuracy of data mapping was maintained.

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. METHODS: Two classification methods are presented: i) a machine learning approach, where detailed features (terms, term n-grams and SNOMED CT concepts) are extracted from death certificates and used to train a set of supervised machine learning models (Support Vector Machines); and ii) a set of keyword-matching rules. These methods were used to identify the presence of diabetes, influenza, pneumonia and HIV in a death certificate. An empirical evaluation was conducted using 340,142 death certificates, divided between training and test sets, covering deaths from 2000-2007 in New South Wales, Australia. Precision and recall (positive predictive value and sensitivity) were used as evaluation measures, with F-measure providing a single, overall measure of effectiveness. A detailed error analysis was performed on classification errors. RESULTS: Classification of diabetes, influenza, pneumonia and HIV was highly accurate (F-measure 0.96). More fine-grained ICD-10 classification effectiveness was more variable but still high (F-measure 0.80). The error analysis revealed that word variations as well as certain word combinations adversely affected classification. In addition, anomalies in the ground truth likely led to an underestimation of the effectiveness. CONCLUSIONS: The high accuracy and low cost of the classification methods allow for an effective means for automatic and real-time surveillance of diabetes, influenza, pneumonia and HIV deaths. In addition, the methods are generally applicable to other diseases of interest and to other sources of medical free-text besides death certificates.