Machine learning results: pay attention to what you don't see - STAT

Even as machine learning and artificial intelligence are drawing substantial attention in health care, overzealousness for these technologies has created an environment in which other critical aspects of the research are often overlooked. There's no question that the increasing availability of large data sources and off-the-shelf machine learning tools offer tremendous resources to researchers. Yet a lack of understanding about the limitations of both the data and the algorithms can lead to erroneous or unsupported conclusions. Given that machine learning in the health domain can have a direct impact on people's lives, broad claims emerging from this kind of research should not be embraced without serious vetting. Whether conducting health care research or reading about it, make sure to consider what you don't see in the data and analyses.