Continuing with my series of meetings with computer science professors from Rice, I met Dr. Akane Sano. She had done large amounts of work incorporating data science and the medical field, analyzing patients' data and keeping automatic records. My meeting with her has created new windows into the medical field of data science.
We mainly talked about the difference of normal machine learning versus adaptive machine learning along with the current uses of digital phenotyping. The main issue with normal machine learning is that changing the input or desired outcome along with the system's operational surrounding can cause massive malfunctions. Since Professor Sano had worked extensively with incorporating data science and medicine, digital phenotyping allows for better record keeping of patients, therefore is very helpful to her.
Dr. Sano's perspective and experiences have shown me how useful data science is in medical fields; along with the complexities of even basic concepts in machine learning.