Tuesday, September 20th
Informatics, coding and pain registries
What are Risk Factors for Painful Polyneuropathy? An Aalysis Using Machine Learning
We are in an era where prescribed and voluntary open data reporting practices, together with a growth in multinational research consortia, are generating medium to large clinical and experimental datasets of varying specificities and quality (in terms of pain information). These datasets provide avenues to explore complex relationships yet need innovative analysis approaches. In this workshop we will explore the application of data analytics to such datasets across a diverse range of pain-related topics, demonstrating the flexibility and power of analytical methods to generate novel insights in both basic science and clinical research. Jan Vollert will open the workshop by giving an introduction into data mining and machine learning, setting foundation knowledge about best practices and pitfalls in a fast-evolving, hot topic. Peter Kamerman will speak on using publicly accessible datasets (e.g., global burden of disease data, NHS England prescribing data, and general health survey data from the US) to gain high-level epidemiological insights into pain burden and the management of pain. Finally, Janne Gierthmühlen will present data on predicting development of chronic pain in longitudinal studies of populations at risk, like diabetes.