New Jersey Maternal Mortality (NJMM) is an interactive web application that visualizes maternal mortality in the state of New Jersey in order to raise awareness of maternal mortality and make it easier to see how it relates to other potential factors (e.g. access to healthcare).

This project focuses on predicting maternal-fetal health outcomes, specifically early detection of preeclampsia, by learning transferable representations from cfRNA and placental transcriptomic data.

A maternal health platform empowering ASHA workers with smart data tools to detect high-risk pregnancies, deliver personalized dietary advice, and ensure safer outcomes in rural India.

Mama's Hub web application used for administration and data collection.

The notebook aims to examine various factors that impact the health of pregnant women and develop a machine learning model to predict the level of health risk for pregnant women.

A repository of CDC National Center for Health Statistics maternal mortality data and a selection of state data, from Michigan, Minnesota and North Carolina, about maternal deaths

This repository contains python programs for data predictions using machine learning algorithms

This project focuses on developing an AI-powered system to predict and prevent pre-eclampsia in women aged 15–45. Pre-eclampsia is a serious pregnancy complication characterized by high blood pressure and potential damage to organs, such as liver and kidneys. Early identification is crucial to improving maternal and neonatal health outcomes.

A lightweight, Rust-based neural network library optimized for multi-modal data processing in low-compute environments, with a focus on maternal health outcome predictions.

Maternal & Newborn Health Support System - A comprehensive healthcare management platform built with Next.js, Prisma, and TypeScript

An intelligent maternal healthcare assistant for expecting mothers, offering personalized tracking, guidance, and support throughout pregnancy and postpartum.

Detection of abnormal delivery patterns and potential obstetric claim anomalies using JKN administrative claims data.

TCC apresentado como requisito parcial para conclusão do curso de Bacharelado em Estatística pela UFES.

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Machine learning-based fetal health classification system using cardiotocography data. Compares multiple algorithms (XGBoost, Random Forest, MLP, Logistic Regression) for predicting fetal health status with 95.9% accuracy.

This is the official repository for NeoMama App.

Dissertação do Dr. Fabiano Elisei Serra.

Statistical analysis of birthweight and related predictors using Python

Lightweight AI assistant for maternal health guidance using ZIP-code data, retrieval-augmented generation, and Colab deployment.