Inferring recommendation interactions in clinical guidelines1
Computer-based techniques to improve medical treatments for people with multiple diseases
What is it about?
An increasing number of patients suffer from multiple diseases at the same time. This makes their treatment much more complex, and the standard medical treatment guidelines no longer apply (they are typically written for patients with just a single disease). We present computer-based techniques for analysing medical guidelines to detect how multiple guidelines may interact in unexpected ways, and how such adverse effects can be recognised and avoided.
Why is it important?
With our ageing population, we have an increasing number of patients that suffer from multiple simultaneious diseases. It's very difficult for doctors to be aware of all the ways in which the treatments for multiple diseases may interact in adverse ways, and have unexpected negative consequences for the patient. Our techniques exploit large knowledge-bases that are available on the Web of Data (Linked Data) to automatically detect and avoid such adverse consequences of interactions between multiple simultaneous treatments.
Resources
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Slides
Linked Open Data for Medical Guidelines Interactions
slide deck by Veruska Zamborlini outlining the content of the paper: how Linked Open Data can be used to detect and avoid unexpected interactions between multiple medical treatments for patients with multiple diseases
