Laura Moss

Research & Development Healthcare Computer Scientist,
NHS Greater Glasgow & Clyde

Honorary Lecturer,
School of Medicine, University of Glasgow

Dr Laura Moss is a Computer Scientist in the Department of Clinical Physics and Bioengineering at NHS Greater Glasgow & Clyde. Dr Moss' principal research field is knowledge engineering and the semantic web, specifically the capture, representation and subsequent refinement of knowledge for use in intelligent medical systems. Dr Moss gained a PhD from the University of Aberdeen in 2010 for work on an approach to the refinement of semantic web knowledge bases, comprising the automatic generation of explanations for anomalies and analogical reasoning. This work was evaluated in the Critical Care medical field. She then moved to NHS Greater Glasgow & Clyde where she became a member of the IDEAS (Intelligent Data Exploration and Analysis) research group, and is currently responsible for leading research into the artificial intelligence fields of knowledge capture & refinement for decision support, hypothesis generation, and intelligent data analysis. In addition, she is a member of the European BrainIT research group and works in collaboration with several external research groups. She has extensive experience of working within multidisciplinary teams. Dr Moss holds honorary research positions in the School of Medicine, University of Glasgow, and Department of Computing Science, University of Aberdeen

CHART-ADAPT: Connecting Healthcare And Research Through A Data Analysis Provisioning Technology

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Semantic Web Approach to Assessing Medical Data Quality

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Argumentation Logic for the Explanation of Medical Anomalies

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Detection and Removal of Inconsistencies (or Biases) during Knowledge Capture.

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Remote Positional Monitoring of Critical Care Patients

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Intelligent Data Analysis

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PhD Thesis: Explaining Anomalies: An Approach to Anomaly-Driven Revision of a Theory

During my PhD I investigated the use of domain knowledge represented in ontologies as a method of generating domain acceptable knowledge base refinements. The use of ontologies avoids the requirement for large datasets and can lead to sophisticated refinements previously unavailable. In the first stage, I investigated the automatic explanation of anomalies as a method of generating refinements to a theory held by a human or computer. Anomalies are interesting as they help to identify the part of the theory requiring refinement. This work involved extensive interviews held with domain experts and the strategies used by the domain experts to provide (appropriate) explanations for the anomalies were identified. A knowledge-based system, EIRA (Explaining, Inferencing, and Reasoning about Anomalies), was developed. EIRA implemented the domain-expert based strategies with domain ontologies and data to automatically generate explanations for an anomaly. To evaluate this approach, EIRA has been applied in the Intensive Care Unit (ICU) domain to investigate the detection and explanation of anomalous patient responses to treatment and ICU clinicians have evaluated the explanations produced by EIRA. In the second stage of my PhD work I explored the application of analogical reasoning in conjunction with domain ontologies to generate refinements.

Peer Reviewed Publications

Conference Abstracts and Posters