# Mother: a maternal online technology for health care dataset

## TL;DR

This paper introduces a maternal health dataset to help build AI models that assist pregnant women, especially in low-resource areas.

## Contribution

The dataset includes 503 validated Q&A pairs from Ugandan pregnant women, tailored for conversational AI in maternal health.

## Key findings

- The dataset was collected from rural and semi-urban Ugandan women with a 94% response rate.
- It addresses common pregnancy concerns and aims to improve maternal health outcomes in low-resource settings.
- Medical professionals validated the answers to ensure accuracy and relevance.

## Abstract

These data enable the development of both textual and speech based conversational machine learning models that can be used by expectant mothers to provide answers to challenges they face during the different trimesters of their pregnancy. Such models are key to the improvement of the lives of pregnant mothers, specifically in low resourced settings where doctors advise is limited by access to hospitals and language barrier. These data were used to develop a conversational chatbot model tailored for mothers in their first, second and third trimesters of pregnancy.

503 question and answer pairs on maternal health were collected through a survey of challenges facing pregnant mothers in a rural and semi-urban area of Uganda. The answers to the questions were provided and validated by professional medical personnel. The participants were purposively sampled, focusing on women in their 1st, 2nd and 3rd trimesters, with a 94% response rate. The dataset addresses common health concerns, symptoms, and conditions associated with pregnancy, particularly for women without immediate access to medical personnel. It targets maternal health outcomes such as pregnancy, morbidity, and mortality, specifically among women of reproductive age.