Elythea is a machine learning tool specifically designed to identify pregnant women at high risk of complications during pregnancy weeks before childbirth.
Its goal is to improve maternal care by addressing preventable complications during pregnancy and reducing pregnancy-related deaths. Elythea focuses on various complications, including postpartum hemorrhage, preterm birth, preeclampsia, eclampsia, and failed vaginal birth after cesarean (VBAC).
The tool offers a three-step process. First, it seamlessly integrates with your electronic health records (EHR) system, whether in the cloud or on-premises, minimizing administrative effort.
Second, Elythea analyzes patients' demographic data, medical history, and existing clinical risk factors to quickly identify high-risk mothers throughout their pregnancy, starting from the first visit.
Lastly, it provides evidence-based recommendations to healthcare providers, enabling early intervention and counseling to prevent costly complications. Elythea has a large amount of patient data, with over 300 million data points used in training the model.
The tool achieves prediction accuracy of up to 85% and an AUC ROC score of up to 0.85. It has been used in clinical studies conducted in various countries. The team behind Elythea includes its founder and CEO, Reetam Ganguli, who has extensive experience in the medical field, and Rishik Lad, co-founder and CTO, who previously worked as a machine learning engineer.
The advisory team consists of medical professionals and machine learning experts. Overall, Elythea aims to improve maternal outcomes, contain healthcare costs, and simplify the lives of healthcare providers by offering a partnership to healthcare providers, employers, and health plans.
