Univfy to Present at Digital Medicine & Medtech Showcase 2018 on How AI and Machine Learning is Maximizing IVF Success for Fertility Patients
LOS ALTOS, Calif., Dec. 19, 2017 /PRNewswire/ -- Univfy® Inc., a predictive analytics company with a mission to expand IVF access and affordability, today announced that Dr. Mylene Yao, co-founder and CEO, will present an overview of the company's business at the Digital Medicine & Medtech Showcase 2018 Conference, where she will be available for one-on-one meetings with investors. The conference will be held January 8-10, 2018 in San Francisco.
Univfy presentation details:
Where: Digital Medicine and Medtech Showcase 2018
Parc 55 San Francisco – Union Square
55 Cyril Magnin Street, B-Cyril Magnin III (4th Floor)
San Francisco, CA 94102
When: Tuesday, January 9, 2018 at 4:30 p.m.
Who: Dr. Mylene Yao, Co-founder and CEO, Univfy Inc.
Progressive fertility centers across the country are using the patented and award-winning Univfy PreIVF Report and the Univfy-Powered IVF Refund Program to make IVF an affordable and realistic option for more fertility patients.
One in eight U.S. couples have infertility. IVF is the most effective fertility treatment for most couples, but the high costs and uncertainty of outcomes keep many couples from having a baby with IVF. Over 60% of fertility patients do not have full insurance coverage for IVF. Patients typically pay more than $10-20K for one IVF treatment and over a third of them may need two or more treatments.
Univfy empowers women and their partners to make confident decisions about fertility treatment by dramatically improving the patient's experience, expanding affordability of having several IVF treatments and maximizing a woman's chance of having a healthy baby from IVF.
Founded by experts in reproductive medicine and machine learning from Stanford University, Univfy has established proprietary technology to develop prediction models of IVF success rates that are validated for individual IVF centers and personalized to each patient based on her health data.