The Underrated Challenges of Building a Learning Model

Nothing about machine learning is necessarily simple, but some aspects may be more difficult than some in healthcare might think. Mark Michalski, the Executive Director of the MGH & BWH Center for Clinical Data Science (CCDS)--a joint projection between Massachusetts General Hospital and Brigham & Women's Hospital--ran the crowd at the AI in Healthcare Summit today in Boston through the more grueling parts of the artificial intelligence (AI) model design process. As applied statistics turn to machine learning and into deep learning and neural networks, the data demands become greater, Michalski said. Neural networks require extensive annotated data, with the optimal word being "annotated." A lot of outside data scientists might see the sheer volume of data that the healthcare industry possesses and think "If only I could get my hands on that, I could…" the speaker said, but what they don't realize is that the majority is unstructured and poorly annotated.