BU-Harvard Team Wins $1.2M NSF Grant to Improve Women’s Reproductive Health using AI and Machine Learning
Researchers to advance distributed analytics to enhance fertility in families
A multidisciplinary team of researchers from Boston University and Harvard University is working to address women’s reproductive health challenges with the help of a $1.2M, four-year grant funded by the National Science Foundation (NSF) through its Smart and Connected Health (SCH) program. The BU-led project will leverage machine learning and artificial intelligence to develop an integrative approach to enable personalized reproductive/fertility predictions and individualized prescriptions to help address fertility problems. The researchers will also focus on improving the understanding of socioeconomic disparities in the use of infertility treatment services.
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, and resulting in more than $5 billion spent annually in infertility services. Such costs are often not covered by health insurance and, consequently, generate access disparities.
“This project is an exciting illustration of the tremendous potential modern data science methods have to leverage the increasing availability of data and impact our health and wellbeing,” says Boston University College of Engineering Professor Ioannis (Yannis) Paschalidis, principal investigator (PI) of the project and director of the Center for Information and Systems Engineering. “With the emergence of personalized medicine, aided by data and algorithmic advances, we now have the ability to learn from available data and develop personalized predictions and intervention recommendations for each individual. The overall goal is to enable people to optimize health before conception, identify modifiable determinants of fertility, and reduce health risks during pregnancy and beyond.”
Innovative team of engineers and clinicians bring a synergistic range of expertise
The BU-Harvard University research team reflects the integrative nature of the project, bringing together algorithms, public health, and medical expertise to address clinical and socio-economic challenges contributing to infertility and women’s reproductive health.
The BU team of principal investigators include: Yannis Paschalidis, BU Professor of Electrical and Computer Engineering, Systems Engineering, and Biomedical Engineering, an expert in decision theory, networks, machine learning, optimization, and computational biology/medicine; Alex Olshevsky, BU Professor of Electrical and Computer Engineering and Systems Engineering, an expert in distributed optimization methods; and Lauren Wise, ScD, BU Professor of Epidemiology, BU School of Public Health, an expert in reproductive epidemiology and PI of the BU Pregnancy Study Online (PRESTO).
Harvard University is the subaward recipient on this grant, and brings the expertise of Co-PI Shruthi Mahalingaiah, Assistant Professor of Environmental Reproductive and Women’s Health. Professor Mahalingaiah is a reproductive endocrinologist, infertility physician scientist and PI of the Ovulation/Menstruation Health Study. Dr. Mahalingaiah holds an adjunct appointment at the BU School of Medicine in the Department of OB/GYN and directs the Program in Environment and Women’s Health, a multi-institutional collaborative effort which includes Boston Medical Center, BU School of Medicine, BU College of Engineering (Paschalidis), and Massachusetts General Hospital, and is housed at the Harvard T.H. Chan School of Public Health.
A shifting paradigm in health data collection
Electronic Health Record (EHR) data are distributed at many locations, hospitals, doctor offices, and other clinics. Medical devices are also becoming smaller with the ability to transmit and store information at home monitoring stations, a patient’s smartphone, and the cloud.
“While traditional learning methods require collecting all data at a central location, this is becoming increasingly harder, even undesirable for privacy reasons,” says Co-PI Alex Olshevsky. “Clearly, the more locations contain sensitive data, the greater is the likelihood of a data breach. In this context, it becomes important to develop fully distributed algorithms that can provide privacy guarantees.”
The researchers will develop an integrative approach to enable personalized reproductive/fertility predictions and prescriptions using distributed, privacy-preserving algorithms trained using multiple data sources. The algorithms will combine 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.
“Collaboration for improving discovery and improving care for women across the lifecourse is critically important,” says Dr. Mahalingaiah. “Merged datasets including self reporting, lifestyle, and exposures, clinical-grade data, and data collected from wearable devices will provide personalized insights so that women can be empowered to understand information on the health of their bodies and make the best choices for their health and futures.”
Predictive fertility/family planning models
The research team will develop fertility/family planning models, including predicting pregnancy, the success of an IVF cycle, the presence of specific reproductive health issues affecting fertility, and making related health care recommendations. Predictive models will identify the most important factors associated with reduced fertility or Assisted Reproductive Technologies (ART) success rates, which could pinpoint specific lifestyle habits, environmental factors, and other key drivers of reduced positive outcomes that can inform health policy recommendations. A key focus will be on ovulation disorders, including Polycystic Ovary Syndrome, which are the leading cause of female infertility and are associated with an increased risk of chronic diseases, such as diabetes and cardiovascular disease.
“Machine learning has the potential to identify novel determinants of subfertility in large prospective cohort studies like PRESTO,” says Dr. Wise. “Developing tools for individuals to quantify their probability of conception based on personal inputs is paradigm-shifting. We are delighted to be partnering with Dr. Paschalidis and his colleagues on this innovative study.”
Professor Paschalidis adds, “It’s one thing to be able to predict one thing will happen, but the important question is, what do you do? How do you improve the condition of the individual? By bringing together this truly multidisciplinary collaboration of clinicians and engineers, this project brings the deep, diverse expertise and requisite resources for advancing the health and wellbeing of women.”
By Maureen Stanton, CISE
