AMJ

The authors present an approach to searching electronic medical records that is based on concept matching rather than keyword matching. Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology.

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The authors present a simple non-probabilistic causal inference model of fluctuating blood sugar level in a diabetes patient using belief revision and update. An action performed by an agent who is trying to model the causal system is followed by an appropriate update of its knowledge. Comparison of the predicted behaviour of the system and the observed outcome leads to further rectification in the agent’s causal model.

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The integration of this tool in the electronic patient record system will help medical practitioners to regularly review patients’ cases with respect to any new literature available. Furthermore it will allow them to improve confidence in their diagnoses and decisions about the treatment, based on the knowledge that they are aware of the recent progress in the field. The doctor can simply focus on his patient rather than search engine peculiarities.

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Evidence Based Medicine is time-consuming as it requires practitioners to search through and extract information from medical research papers. Automatic summarisation techniques can benefit practitioners by extracting relevant information associated with their queries. The authors show that the use of specialised corpora and domain knowledge can help identify useful information in medical text.

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To help the physician, the authors propose the development of query-based multi-document summarisation systems that, given a clinical question, find the relevant documents, appraise their medical quality, and summarise them within the context of the question. The expected output of such systems would be synthesised summaries that highlight the key answers to the clinical question as given by the medical literature.

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Retinal image registration methods provide tools for retinal image information fusion and comparison. This study proposes two registration methods for retinal image registration and longitudinal comparison with large image over-lapping area.The methods target a telemedicine application in a clinical environment.

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This study reports self-care pattern among advanced prostate cancer patients by conducting a survey and specifies the requirements for an online system based upon survey findings and data analysis. The study describes a case-based reasoning approach for improving accessible decision support information to the patients in an online PHR environment. The study aims to make a contribution to the patient-driven online health data sharing solutions.

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The Artificial Intelligence in Health 2011 workshop was a first of its kind, national initiative that aimed to bring together scholars and practitioners in the field of AI-driven health informatics to present and discuss their research, share knowledge and experience, define key research challenges and explore possible collaborations to advance e-Health development nationally and internationally.We hope that the breadth and diversity of the papers presented at the workshop and published in this special issue will foster further collaboration and AI driven research in health.

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Incomplete requisition forms: Potential for error in the microbiology laboratory;Prevalence of thyroid disorders among the ante-natal population; Doctoring in cross-racial contexts: Highlighting the role of colour vision; Representation of assertions in clinical free-text using SNOMED CT.

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