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Kampal Artificial Intelligence for rare disease diagnosis

Societal challenges

The Spanish Foundation for the Study and Treatment of Gaucher Disease and other Lysosomal Diseases (FEETEG) supports the scientific research of Gaucher disease and its treatment methods. The Foundation is interested in predicting the probability of development of diseases such as neoplasms or Parkinson’s disease in patients of Gaucher disease (correlations between diseases). For this purpose, Kampal Data Solutions was contacted by FEETEG to develop an advanced analytical model based on Artificial Intelligence with the information available in the Gaucher Spanish Disease Registry.

Technical challenges

Due to the fact that Gaucher disease is a rare disease with few national registries, the computational power of a local computer for the study of correlations with other diseases was enough to analyse the data collected. The challenge is to generate a new model able to predict if a person has the probability of developing Gaucher disease. In this case, the AI model must include not only data from current Gaucher disease patients but also data from healthy patients. Opening our sample universe also to healthy patients exponentially increases the sample size and potentially the model’s complexity. This implies the need of advanced computational resources such as the cloud platform provided by EOSC.

How EOSC can help and add value

In the context of the EOSC-hub project, Kampal Data Solutions is benefitting from storing the healthy and ill patients’ registries to a database on EOSC infrastructure. EOSC also supports the pilot as it statistically analyses the data and develops a classifying model based on machine learning techniques. It is also optimising a machine learning algorithm for a cloud based environment and validating the model performance and producing plots /charts of diverse KPIs.

The EOSC Portal is operated by the EOSC Enhance (Grant Agreement no. 871160), EOSC-hub (Grant Agreement no. 777536), and OpenAIRE-Advance (Grant Agreement no. 777541) projects funded by the European Union’s Horizon 2020 research and innovation programme. For a complete list of contributors, visit the About EOSC Portal