Progress in machine learning and artificial intelligence will be critical in the coming years to better understand the mechanisms of disease, which will in turn enable us to create more efficacious therapies for patients. The drug development cycle entails many steps where large amounts of valuable data are collected in the context of clinical trials. Working on this data provides us with potential treatment targets, new biomarkers, as well as other information that enables us to identify which patients will benefit most from a given treatment. Additionally, safety and efficacy information is collected. After a drug enters the market, further data is generated and collected in the form of electronic medical records, disease registries, health insurance claims, surveys, digital devices and sensors, among others.
In recent years the availability of healthcare data in large quantities, as well as in diverse data modalities and data sources has introduced new opportunities but also difficult challenges. In addition, the use of the previously mentioned data sources has steadily increased. Using machine learning-based methodologies could help to extract knowledge and enable learning from these increasingly heterogeneous data sources. The use of these innovative methods has shown the potential to revolutionize medical practice and enable us to develop personalized medicines.
This workshop will invite experts from both industry and academia to share their research and experience in using artificial intelligence and machine learning methods in pharmaceutical research and development.