Mother: a maternal online technology for health care dataset
Abstract Objectives 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...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Research Notes |
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| Online Access: | https://doi.org/10.1186/s13104-025-07230-2 |
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| author | Odongo Steven Eyobu Brian Angoda Nyanga Lukman Bukenya Daniel Ongom Tonny J. Oyana |
| author_facet | Odongo Steven Eyobu Brian Angoda Nyanga Lukman Bukenya Daniel Ongom Tonny J. Oyana |
| author_sort | Odongo Steven Eyobu |
| collection | DOAJ |
| description | Abstract Objectives 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. Data description 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. |
| format | Article |
| id | doaj-art-e7c1dbd10bb444ddaf670e40ece4ae52 |
| institution | DOAJ |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj-art-e7c1dbd10bb444ddaf670e40ece4ae522025-08-20T03:06:48ZengBMCBMC Research Notes1756-05002025-04-011811310.1186/s13104-025-07230-2Mother: a maternal online technology for health care datasetOdongo Steven Eyobu0Brian Angoda Nyanga1Lukman Bukenya2Daniel Ongom3Tonny J. Oyana4Geospatial Data and Computational Intelligence lab, College of Computing and Information Science, Makerere UniversityGeospatial Data and Computational Intelligence lab, College of Computing and Information Science, Makerere UniversityGeospatial Data and Computational Intelligence lab, College of Computing and Information Science, Makerere UniversityGeospatial Data and Computational Intelligence lab, College of Computing and Information Science, Makerere UniversityGeospatial Data and Computational Intelligence lab, College of Computing and Information Science, Makerere UniversityAbstract Objectives 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. Data description 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.https://doi.org/10.1186/s13104-025-07230-2Maternal healthPregnancyQuestion and answer knowledge baseElectronic health (E-health)Conversational chatbots |
| spellingShingle | Odongo Steven Eyobu Brian Angoda Nyanga Lukman Bukenya Daniel Ongom Tonny J. Oyana Mother: a maternal online technology for health care dataset BMC Research Notes Maternal health Pregnancy Question and answer knowledge base Electronic health (E-health) Conversational chatbots |
| title | Mother: a maternal online technology for health care dataset |
| title_full | Mother: a maternal online technology for health care dataset |
| title_fullStr | Mother: a maternal online technology for health care dataset |
| title_full_unstemmed | Mother: a maternal online technology for health care dataset |
| title_short | Mother: a maternal online technology for health care dataset |
| title_sort | mother a maternal online technology for health care dataset |
| topic | Maternal health Pregnancy Question and answer knowledge base Electronic health (E-health) Conversational chatbots |
| url | https://doi.org/10.1186/s13104-025-07230-2 |
| work_keys_str_mv | AT odongosteveneyobu motheramaternalonlinetechnologyforhealthcaredataset AT brianangodanyanga motheramaternalonlinetechnologyforhealthcaredataset AT lukmanbukenya motheramaternalonlinetechnologyforhealthcaredataset AT danielongom motheramaternalonlinetechnologyforhealthcaredataset AT tonnyjoyana motheramaternalonlinetechnologyforhealthcaredataset |