A multidisciplinary team of researchers from Boston University and Harvard University is working to address reproductive health and fertility challenges using machine learning and artificial intelligence, with the help of a $1.2 million, four-year grant funded by the National Science Foundation (NSF) through its Smart and Connected Health (SCH) program.
The BU-led project will use distributed, privacy-preserving algorithms trained using multiple data sources, combining information from self-administered surveys, electronic health records, and personal health records to produce highly accurate personalized predictions and prescriptions or recommendations, enabling individuals and their physicians to make the most appropriate, individualized health care decisions.
The team of principal investigators (PIs) includes Dr. Lauren Wise, professor of epidemiology at Boston University School of Public Health (BUSPH) and principal investigator of the BUSPH-based Pregnancy Study Online (PRESTO).
“Machine learning has the potential to identify novel determinants of subfertility in large prospective cohort studies like PRESTO,” Dr. Wise says. “Developing tools for individuals to quantify their probability of conception based on personal inputs is paradigm-shifting.”Friday Letter Submission, Publish on November 15