About BabySafe Health
BabySafe Health was founded in 2020 by a group of 4 Rutgers Freshmen with the mission of giving all babies an equal opportunity to live a healthy life. The company started after participating and winning the J&J Black Tech Health Hackathon partnered with Amazon Web Services by proposing the idea of utilizing machine learning to lessen maternal health disparities. After being exposed to the devastating statistic that, in New Jersey, for every white infant that dies, over 3 black infants die, we were motivated to create our solution to have an impact on society. Our objective is to develop an application that utilizes a machine-learning algorithm to analyze past infant mortality data to provide mothers with insight into their pregnancy’s potential health complications and recommend actions to remedy these complications. Although this app would help all expecting mothers, it is primarily targeted towards women of color to close the health disparity between minority and white communities as well as alleviate the impact of social determinants on health. It is our aspiration that by making resources more readily available to our users, social determinants will have less of an impact on pregnancies, and we can minimize the disparity in pregnancy outcomes.
TechUnited Better Wellness Challenge @ Propelify 2021
We had the pleasure of pitching at the TechUnited Better Wellness Challenge @ Propelify 2021 in front of top executives within RWJBarnabas Health, Colgate-Palmolive, & Labcorp and won $25,000.
Rutgers NSF I-Corps
Rutgers NSF (National Science Foundation) I-Corps is a program that enables student entrepreneurs to gain insight into starting a business through the Customer Discovery Process. The program enables us to develop an innovation via the scientific process to release a solution that benefits society. We've conducted more than 25 interviews with pregnant mothers, mothers that have gone through the pregnancy process, doctors, and nurses. We're using the insight we've gained from these interviews to create an application that puts mothers first.
IDEA Program Design Sprint with Johnson and Johnson and AWS Black Tech Health Hackathon
At the Johnson and Johnson Black Tech Hackathon, we developed a machine learning predictive analysis model that analyzed 144,000 rows and 50 columns of the dataset provided to predict with 91% accuracy the leading cause of infant death. We then collaborated to address the health disparity concerns raised by the model between black and white communities through implementation of machine learning algorithms in imaging diagnosis and hospital resources allocation to facilitate a more engaging doctor-patient relationship driven by data science. Our presentation and solution won first place in our specific problem category as well as the hackathon overall.