Evidence synthesis is a key element of evidence-based medicine. However, it is currently hampered by being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date. To overcome this, a variety of techniques are being explored, including using automation technology. Here, we describe a fully automated evidence synthesis system for intervention studies, one that identifies all the relevant evidence, assesses the evidence for reliability and collates it to estimate the relative effectiveness of an intervention. Techniques used include machine learning, natural language processing and rule-based systems. Results are visualised using modern visualisation techniques. We believe this to be the first, publicly available, automated evidence synthesis system: an evidence mapping tool that synthesises evidence on the fly.

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Personal data is a necessity in many fields for research and innovation purposes, and when such data is shared, the data controller carries the responsibility of protecting the privacy of the individuals contained in their dataset. The removal of direct identifiers, such as full name and address, is not enough to secure the privacy of individuals as shown by de-anonymisation methods in the scientific literature. Data controllers need to become aware of the risks of de-anonymisation and apply the appropriate anonymisation measures before sharing their datasets, in order to comply with privacy regulations. To address this need, we defined a procedure that makes data controllers aware of the de-anonymisation risks and helps them in deciding the anonymisation measures that need to be taken in order to comply with the General Data Protection Regulation (GDPR). We showcase this procedure with a customer relationship management (CRM) dataset provided by a telecommunications provider. Finally, we recount the challenges we identified during the definition of this procedure and by putting existing knowledge and tools into practice.

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