母亲:医疗保健数据集的母性在线技术
Odongo Steven Eyobu1,2, Brian Angoda Nyanga3,4, Lukman Bukenya3,4
1Geospatial Data and Computational Intelligence lab, College of Computing and Information Science, Makerere University, P.O Box, 7062, Kampala, Uganda. odongo.eyobu@mak.ac.ug.
这项研究收集了来自503名乌干达妇女的孕产妇健康数据,以创建人工智能驱动的聊天机器人. 这些工具将为孕妇提供关键的健康信息,特别是在服务不足的地区.
科学领域:
- 孕产妇健康 孕产妇健康
- 机器学习 机器学习
- 数字健康数字健康
背景情况:
- 在资源较低的环境中,有限的医疗咨询机会给孕妇带来了挑战.
- 语言障碍可能会进一步限制获得重要的孕产妇医疗保健信息的机会.
- 现有的医疗保健系统可能无法充分满足农村和半城市地区孕妇的特殊需求.
研究的目的:
- 为孕妇开发基于文本和语音的对话机器学习模型.
- 提供可访问的答案,在怀孕期间面临的挑战,在所有的三个月.
- 通过人工智能驱动的支持,改善资源有限的环境中的孕产妇健康结果.
主要方法:
- 通过乌干达的一项调查,收集了503对关于母亲健康挑战的问答对.
- 通过专业医疗人员验证答案.
- 在第一,第二和第三季度有意抽样女性,达到94%的响应率.
主要成果:
- 收集了503对母亲健康问答的数据集.
- 数据解决了常见的怀孕问题,症状和条件.
- 该数据集是为那些无法立即接触医疗人员的妇女量身定制的.
结论:
- 开发的数据集可以为孕妇创建对话式AI模型.
- 这些人工智能模型可以提供及时和相关的健康信息,克服访问和语言障碍.
- 该倡议旨在改善孕产妇健康,降低发病率,改善生育年龄妇女的死亡率.
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