Tag: interpretability

Connecting with the health data science community in Spain

By Anna Spyker, Software Engineer Recently I attended the IEEE CBMS Conference in Cordoba, Spain to connect with other researchers, data scientists and clinicians and share the work we’ve been doing on interpretable machine learning. I felt an incredible sense of community at the conference. As it was a smaller conference, there was a lot …

Interpretable Machine Learning

The “black box” metaphor is commonly used to refer to the lack of understanding of how modern Machine Learning (ML) systems make decisions.  Researchers are actively trying to remedy this situation which is especially problematic in healthcare, largely because legal accountability and ethics have greater emphasis and importance in the healthcare decision-making process than in …

Missing Health Data Imputation

Medical professionals analysing electronic health data often have to deal with data sets that are incomplete. If this data is not handled correctly it can result in negative outcomes such as bias, complications and ultimately invalid conclusions. Finding ways to deal with missing data is at the heart of the project Value added by Multiple …