Digital Technologies in Maternal Healthcare: A Thematic Review of mHealth, Telehealth and Artificial Intelligence
DOI:
https://doi.org/10.54536/jhmhe.v1i1.8884Keywords:
Artificial Intelligence, Maternal Health, Mhealth, Telehealth, Thematic AnalysisAbstract
Digital health has evolved through multiple technological generations from machine learning prediction, but these technologies have never been synthesized as a single trajectory. Thus, this paper provides a thematic review of the evolutionary progression of digital health interventions in maternal care, with a focus on continuity and emergence of themes. Searches were conducted using structured search protocols for mHealth, telehealth, data infrastructure, artificial intelligence predictions, decision support systems, cosnversational agents, remote monitoring, equitable delivery of care and governance. Records were screened for relevance to digital maternal care, and thematic synthesis was performed on 67 sources that met the criteria for the study. The synthesis was organized by generation and coded for continuity vs. emergence. Ten themes were identified across all 67 sources. While mHealth improved attendance at antenatal appointments and service linkage, most studies had poor methodological quality and did not assess mortality effects directly. Telehealth demonstrated safety at scale, with 45.94% of antenatal consultations provided via video, without significant change in preeclampsia, fetal growth restriction, preterm birth or stillbirth. Artificial intelligence-based prediction studies reported strong discrimination (AUROC = 0.7–0.9; median = 0.8); however, only 11 of 28 studies (39%) provided external validation, and data completeness in one national maternal electronic record reached 28%. Themes such as equity, infrastructure, data quality and clinician trust were consistent throughout all three generations. Calibration, explainability, algorithmic bias, federated learning and large-language-model governance were identified as emerging themes during the transition from mHealth to AI. Thus, instead of resolving the field’s core problems, each successive technology generation amplified those same issues rather than replacing them. The field’s current constraints remain workflow integration and data quality.
References
Abraham, A., Le, B. L., Kosti, I., Straub, P., Velez-Edwards, D., Davis, L. K., Newton, J. M., Muglia, L. J., Rokas, A., Bejan, C. A., Sirota, M., & Capra, J. A. (2022). Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth. BMC Medicine, 20(1), 333. https://doi.org/10.1186/s12916-022-02522-x
Aifuobhokhan, J., Ogunjinmi, A., Agbarakwe, C. A., Oduguwa, D. O., Essiet, A. P., Osunkiyesi, T., & Modesire, A. (2025). Artificial Intelligence-Powered Risk Prediction Models for Preventable Maternal Mortality in Rural Settings: A Systematic Review. In Research Square. https://doi.org/10.21203/rs.3.rs-7973881/v1
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M., Ahmed, M. A. A., Fleet, R., & Fortin, J. (2020). Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Globalization and Health, 16(1), 52. https://doi.org/10.1186/s12992-020-00584-1
Ames, H. M., Glenton, C., Lewin, S., Tamrat, T., Akama, E., & Leon, N. (2019). Clients’ perceptions and experiences of targeted digital communication accessible via mobile devices for reproductive, maternal, newborn, child, and adolescent health: a qualitative evidence synthesis. Cochrane Database of Systematic Reviews, 2019(10). https://doi.org/10.1002/14651858.cd013447
Amil, S., Da, S. M. A. R., Plaisimond, J., Roch, G., Sasseville, M., Bergeron, F., & Gagnon, M. (2025). Interactive Conversational Agents for Perinatal Health: A Mixed Methods Systematic Review. Healthcare, 13(4), 363. https://doi.org/10.3390/healthcare13040363
Amoakoh, H. B., Klipstein-Grobusch, K., Grobbee, D. E., Amoakoh-Coleman, M., Oduro-Mensah, E., Sarpong, C., Frimpong, E., Kayode, G. A., Agyepong, I. A., & Ansah, E. (2019). Using Mobile Health to Support Clinical Decision-Making to Improve Maternal and Neonatal Health Outcomes in Ghana: Insights of Frontline Health Worker Information Needs. JMIR Mhealth and Uhealth, 7(5). https://doi.org/10.2196/12879
Amoakoh-Coleman, M., Borgstein, A. B. J., Sondaal, S. F. V., Grobbee, D. E., Miltenburg, A. S., Verwijs, M., Ansah, E., Browne, J. L., & Klipstein-Grobusch, K. (2016). Effectiveness of mHealth Interventions Targeting Health Care Workers to Improve Pregnancy Outcomes in Low- and Middle-Income Countries: A Systematic Review. Journal of Medical Internet Research, 18(8). https://doi.org/10.2196/jmir.5533
Andonotopo, W., Bachnas, M. A., Pramono, M. B. A., Dewantiningrum, J., Sanjaya, I. N. H., Darmawan, E., Akbar, M. I. A., Aldiansyah, D., Yeni, C. M., Bernolian, N., Andanaputra, W. E. K., & Stanojevic, M. (2025). Artificial Intelligence in Perinatal Medicine: A Systematic Review of Current Applications, Limitations, and a Translational Roadmap for the Foundation-Model Era. Dr Sulaiman Al Habib Medical Journal, 7(4), 214–231. https://doi.org/10.4103/dshmj.dshmj_49_25
Asamoah, A. (2025). Digital Health Innovations and their Impact on Reducing Maternal and Infant Mortality: A Global Systematic Review of Successes, Challenges, and Best Practices. International Journal of Scientific Research and Modern Technology., 17–25. https://doi.org/10.38124/ijsrmt.v4i7.655
Atkinson, J., Hastie, R., Walker, S., Lindquist, A., & Tong, S. (2023). Telehealth in antenatal care: recent insights and advances. BMC Medicine, 21(1), 332. https://doi.org/10.1186/s12916-023-03042-y
Badisy, I. E., Assarag, B., & Belrhiti, Z. (2026). Interpretable clinical decision support systems in high-risk pregnancy: a scoping review of models, methods, and implementation. BMC Pregnancy and Childbirth, 26(1), 125. https://doi.org/10.1186/s12884-025-08614-9
Benjamin, H., Vwalika, B., Killam, W. P., Wamalume, C., Giganti, M. J., Mbewe, R., Stringer, E. M., Chintu, N., Putta, N., Liu, K. C., Chibwesha, C. J., Rouse, D. J., & Stringer, J. S. A. (2011). Implementation of the Zambia Electronic Perinatal Record System for comprehensive prenatal and delivery care. International Journal of Gynecology & Obstetrics, 113(2), 131–136. https://doi.org/10.1016/j.ijgo.2010.11.013
Bertini, A., Salas, R., Chabert, S., Sobrevía, L., & Pardo, F. (2022). Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Frontiers in Bioengineering and Biotechnology, 9, 780389. https://doi.org/10.3389/fbioe.2021.780389
Bossman, E., Johansen, M. A., & Zanaboni, P. (2022). mHealth interventions to reduce maternal and child mortality in Sub-Saharan Africa and Southern Asia: A systematic literature review. Frontiers in Global Women s Health, 3, 942146. https://doi.org/10.3389/fgwh.2022.942146
Charifson, M., Wen, T., Zell, B., Vaidya, P., Rios, C. I., Fagbohun, C. F., & Fulcher, I. (2024). Impact of Remote Blood Pressure Monitoring Device Connectivity on Engagement Among Pregnant Individuals Enrolled in the Delfina Care Platform: Observational Study. JMIR Mhealth and Uhealth, 12. https://doi.org/10.2196/55617
Chung, K., Cho, H. Y., & Park, J. Y. (2020). A Chatbot for Perinatal Women’s and Partners’ Obstetric and Mental Health Care: Development and Usability Evaluation Study. JMIR Medical Informatics, 9(3). https://doi.org/10.2196/18607
Dlugatch, R., Georgieva, A., & Kerasidou, A. (2024). AI-driven decision support systems and epistemic reliance: a qualitative study on obstetricians’ and midwives’ perspectives on integrating AI-driven CTG into clinical decision making. BMC Medical Ethics, 25(1), 6. https://doi.org/10.1186/s12910-023-00990-1
Du, Y., Antoniadi, A. M., McNestry, C., McAuliffe, F. M., & Mooney, C. (2022). The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations. Applied Sciences, 12(20), 10323. https://doi.org/10.3390/app122010323
Dzabeng, F., Enuameh, Y., Adjei, G., Manu, A., Asante, K. P., & Owusu-Agyei, S. (2016). Community health workers’ experiences of mobile device-enabled clinical decision support systems for maternal, newborn and child health in developing countries. The JBI Database of Systematic Reviews and Implementation Reports, 14(9), 57–64. https://doi.org/10.11124/jbisrir-2016-003076
Gaudensia, O., Rachel, M., Karen, Y., Charles, M., Simon, W., S, K., Ali, & T, M., Blandina. (2025). Healthcare Providers’ Experiences and Perspectives on the Use of Electronic Health Record System-ULTRA for Maternal and Perinatal Care in Kilimanjaro, Tanzania. A qualitative study [Data set]. In Figshare. Figshare (United Kingdom). https://doi.org/10.6084/m9.figshare.30880457
Girmay, M. (2024). Digital Health Divide: Opportunities for Reducing Health Disparities and Promoting Equitable Care for Maternal and Child Health Populations. PubMed Central, 13. https://doi.org/10.25259/ijma_41_2024
Gitonga, E., Nyamari, J., Warutere, P., & Wanyoro, A. (2021). Influence of short message service reminders on utilisation of focused antenatal care among women in rural Kenya: a randomised controlled trial. African Journal of Midwifery and Women s Health, 15(2), 1–11. https://doi.org/10.12968/ajmw.2020.0014
Haskew, J., Rø, G., Saito, K., Turner, K., Odhiambo, G., Wamae, A., Sharif, S., & Sugishita, T. (2015). Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya. International Journal of Medical Informatics, 84(5), 349–354. https://doi.org/10.1016/j.ijmedinf.2015.01.005
Horgan, R., Nehme, L., & Abuhamad, A. (2023). Artificial intelligence in obstetric ultrasound: A scoping review. Prenatal Diagnosis, 43(9), 1176–1219. https://doi.org/10.1002/pd.6411
Joel, K. K., Baloch, F. A. Q., & Aleko, T. (2026). The Impact of Digital Health Technologies on Reproductive Health Equity: A Scoping Review. International Journal of Innovative Science and Research Technology (IJISRT), 1852. https://doi.org/10.38124/ijisrt/26mar717
Jones, R. D., Allison, M. K., Moody, H., Peng, C., & Eswaran, H. (2023). Use of Cellular-Enabled Remote Patient Monitoring Device for Hypertension Management in Pregnant Women: A Feasibility Study. Maternal and Child Health Journal, 27(7), 1191–1198. https://doi.org/10.1007/s10995-023-03628-1
Kabongo, E. M., Mukumbang, F. C., Delobelle, P., & Nicol, E. (2021). Explaining the impact of mHealth on maternal and child health care in low- and middle-income countries: a realist synthesis. BMC Pregnancy and Childbirth, 21(1), 196. https://doi.org/10.1186/s12884-021-03684-x
Kanayan, S. (2025). Artificial Intelligence in Maternal Health Forecasting in Low- and Middle-Income Countries: A Systematic Narrative Review of Enablers, Barriers, and Policy Recommendations. In Research Square. https://doi.org/10.21203/rs.3.rs-7440020/v1
Khalil, A., Perry, H., Lanssens, D., & Gyselaers, W. (2019). Telemonitoring for hypertensive disease in pregnancy. Expert Review of Medical Devices, 16(8), 653–661. https://doi.org/10.1080/17434440.2019.1640116
Kitt, J., Fox, R., Tucker, K. L., & McManus, R. J. (2019). New Approaches in Hypertension Management: a Review of Current and Developing Technologies and Their Potential Impact on Hypertension Care. Current Hypertension Reports, 21(6), 44. https://doi.org/10.1007/s11906-019-0949-4
Knop, M., Hayashi, M., Lin, R., Saing, C. H., Ung, M., Oy, S., Yam, E. L. Y., Zahari, M., & Yi, S. (2024). Impact of mHealth interventions on maternal, newborn, and child health from conception to 24 months postpartum in low- and middle-income countries: a systematic review. BMC Medicine, 22(1), 196. https://doi.org/10.1186/s12916-024-03417-9
Koivu, A., & Sairanen, M. (2020). Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Information Science and Systems, 8(1), 14. https://doi.org/10.1007/s13755-020-00105-9
Konnyu, K. J., Danilack, V. A., Adam, G. P., Peahl, A., Cao, W., & Balk, E. M. (2023). Changes to Prenatal Care Visit Frequency and Telehealth. Obstetrics and Gynecology, 141(2), 299–323. https://doi.org/10.1097/aog.0000000000005046
Labrique, A., Wadhwani, C., Williams, K. A., Lamptey, P., Hesp, C., Luk, R., & Aerts, A. (2018). Best practices in scaling digital health in low and middle income countries. Globalization and Health, 14(1), 103. https://doi.org/10.1186/s12992-018-0424-z
Lee, S. H., Nurmatov, U., Nwaru, B. I., Mukherjee, M., Grant, L., & Pagliari, C. (2015). Effectiveness of mHealth interventions for maternal, newborn and child health in low– and middle–income countries: Systematic review and meta–analysis. Journal of Global Health, 6(1), 10401. https://doi.org/10.7189/jogh.06.010401
Lee, V. V., Vijayakumar, S., Ng, W. Y., Lau, N. Y., Leong, Q. Y., Ooi, D. S. Q., Su, L. L., Lee, Y. S., Chan, S., Blasiak, A., & Ho, D. (2023). Personalization and localization as key expectations of digital health intervention in women pre- to post-pregnancy. Npj Digital Medicine, 6(1), 183. https://doi.org/10.1038/s41746-023-00924-6
Li, S., Wang, Z., Vieira, L., Zheutlin, A. B., Ru, B., Schadt, E., Wang, P., Copperman, A. B., Stone, J. L., Gross, S. J., Kao, Y.-H., Lau, Y. K., Dolan, S. M., Schadt, E. E., & Li, L. (2022). Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data. Npj Digital Medicine, 5(1), 68. https://doi.org/10.1038/s41746-022-00612-x
Lund, S., Nielsen, B., Hemed, M., Boas, I. M., Said, A., Said, K., Makungu, M. H., & Rasch, V. (2014). Mobile phones improve antenatal care attendance in Zanzibar: a cluster randomized controlled trial. BMC Pregnancy and Childbirth, 14(1), 29. https://doi.org/10.1186/1471-2393-14-29
Malloy, S. (2024). Impact of Digital Health Interventions on Birth Equity: A Review. Seminars in Reproductive Medicine, 42(2), 140–150. https://doi.org/10.1055/s-0044-1791206
Manvi, S., Deva, R., Madhiwalla, N., & Ismail, A. (2025). “Who Has the Time?”: Understanding Receptivity to Health Chatbots among Underserved Women in India. In ArXiv.org. https://doi.org/10.48550/arxiv.2502.15978
Masoi, T. J., Kibusi, S. M., Bintabara, D., & Lilungulu, A. (2023). The effectiveness of interactive mobile health technologies in improving antenatal care service utilization in Dodoma region, Tanzania: A quasi—Experimental study. PubMed Central, 2(8). https://doi.org/10.1371/journal.pdig.0000321
McNabb, M., Chukwu, E., Ojo, O., Shekhar, N., Gill, C., Salami, H., & Jega, F. (2015). Assessment of the Quality of Antenatal Care Services Provided by Health Workers Using a Mobile Phone Decision Support Application in Northern Nigeria: A Pre/Post-Intervention Study. PLoS ONE, 10(5). https://doi.org/10.1371/journal.pone.0123940
Modi, D., Dholakia, N., Gopalan, R., Venkatraman, S., Dave, K., Shah, S., Desai, G., Qazi, S., Sinha, A., Pandey, R. M., Anand, A., Desai, S., & Shah, P. (2019). mHealth intervention “ImTeCHO” to improve delivery of maternal, neonatal, and child care services—A cluster-randomized trial in tribal areas of Gujarat, India. PLoS Medicine, 16(10). https://doi.org/10.1371/journal.pmed.1002939
Mohamed, H., Ismail, A., Sutan, R., Rahman, R. A., & Juval, K. (2025). A scoping review of digital technologies in antenatal care: recent progress and applications of digital technologies. BMC Pregnancy and Childbirth, 25(1), 153. https://doi.org/10.1186/s12884-025-07209-8
Mohit, M. L. S., Rahul, R. K. M., & Bafila, P. (2026). UttaraRisk-Next: A Multi-Task Ensemble Learning Framework for Maternal Health Risk Prediction. INTELIGENCIA ARTIFICIAL, 29(77), 151–181. https://doi.org/10.4114/intartif.vol29iss77pp151-181
Montenegro, J. L. Z., Costa, C. A. da, & Janssen, L. P. (2022). Evaluating the use of chatbot during pregnancy: A usability study. Healthcare Analytics, 2, 100072. https://doi.org/10.1016/j.health.2022.100072
Mosaly, P., Tracton, G., Thummala, M. R., Shieh, K., & Stuebe, A. (2025). Human Factors Methods in Developing AI and Machine Learning High-Risk Prediction Models in Obstetric Care. AHFE International, 192. https://doi.org/10.54941/ahfe1006635
Mugoye, K., Okoyo, H. O., & Mcoyowo, S. (2019). Smart-bot Technology: Conversational Agents Role in Maternal Healthcare Support. 1–7. https://doi.org/10.23919/istafrica.2019.8764817
Nigussie, Z. Y., Zemicheal, N., Tiruneh, G. T., Bayou, Y. T., Teklu, G. A., Kibret, E. S., Eifler, K., Hodsdon, S. E., Altaye, D. E., Rosenblum, L., Getu, Y. A., Nebi, Z., Lemango, E. T., Kebede, E., & Betemariam, W. (2021). Using mHealth to Improve Timeliness and Quality of Maternal and Newborn Health in the Primary Health Care System in Ethiopia. Global Health Science and Practice, 9(3), 668–681. https://doi.org/10.9745/ghsp-d-20-00685
Oliveira-Ciabati, L., Vieira, C. S., Franzon, A. C. A., Alves, D., Zaratini, F. S., Braga, G. C., Sanchez, J. A. C., Bonifácio, L. P., Andrade, M. S., Fernandes, M., Quintana, S. M., Fábio, S. V., Pileggi, V. N., Vieira, E. M., & Souza, J. P. (2017). PRENACEL – a mHealth messaging system to complement antenatal care: a cluster randomized trial. Reproductive Health, 14(1), 146. https://doi.org/10.1186/s12978-017-0407-1
Pfitzner, B., Steckhan, N., & Arnrich, B. (2021). Federated Learning in a Medical Context: A Systematic Literature Review. ACM Transactions on Internet Technology, 21(2), 1–31. https://doi.org/10.1145/3412357
Ranaei-Zamani, N., David, A. L., Siassakos, D., Dadhwal, V., Melbourne, A., Aughwane, R., Russell-Buckland, J., Tachtsidis, I., Hillman, S., & Mitra, S. (2024). Saving babies and families from preventable harm: a review of the current state of fetoplacental monitoring and emerging opportunities. Npj Women s Health, 2(1). https://doi.org/10.1038/s44294-024-00015-1
Rasoli, R., Ebrahimisadrabadi, F., Khedri, Z., & Sohrabei, S. (2026). Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review. Oxford Open Digital Health, 4. https://doi.org/10.1093/oodh/oqag001
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. Npj Digital Medicine, 3(1), 119. https://doi.org/10.1038/s41746-020-00323-1
S, B., S, S., & Maknikar, S. (2026). Artificial Intelligence for Predicting and Managing High-Risk Pregnancies in Low- and Middle-Income Countries: A Scoping Review. In Open Science Framework. https://doi.org/10.17605/osf.io/2tyu7
Samaran, E. (2024). Integration of Digital Health Technologies in Maternal and Child Health Management: Opportunities and Challenges. Join Journal of Social Science, 1(4), 554–566. https://doi.org/10.59613/grgwwt17
Sondaal, S. F. V., Browne, J. L., Amoakoh-Coleman, M., Borgstein, A. B. J., Miltenburg, A. S., Verwijs, M., & Klipstein-Grobusch, K. (2016). Assessing the Effect of mHealth Interventions in Improving Maternal and Neonatal Care in Low- and Middle-Income Countries: A Systematic Review. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0154664
Susilowati, D., Sriatmi, A., Arso, S. P., & Kartasurya, M. I. (2025). A Scoping Review on the Effect of Digital Health Use for Maternal Care. BIO Web of Conferences, 193, 72. https://doi.org/10.1051/bioconf/202519300072
Swartz, A., LeFevre, A., Perera, S., Kinney, M., & George, A. (2021). Multiple pathways to scaling up and sustainability: an exploration of digital health solutions in South Africa. Globalization and Health, 17(1), 77. https://doi.org/10.1186/s12992-021-00716-1
Sylvain, M. H., Nyabyenda, E. C., Uwase, M., Komezusenge, I., Ndikumana, F., & Ngaruye, I. (2025). Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda. BMC Medical Informatics and Decision Making, 25(1), 76. https://doi.org/10.1186/s12911-025-02921-z
Thirugnanasundralingam, K., Davies-Tuck, M., Rolnik, D. L., Reddy, M., Mol, B. W., Hodges, R., & Palmer, K. R. (2023). Effect of telehealth-integrated antenatal care on pregnancy outcomes in Australia: an interrupted time-series analysis. The Lancet Digital Health, 5(11). https://doi.org/10.1016/s2589-7500(23)00151-6
Till, S., Mkhize, M., Farao, J., Shandu, L. D., Muthelo, L., Coleman, T., Mbombi, M. O., Bopape, M. A., Klingberg, S., Heerden, A. van, Mothiba, T. M., Densmore, M., Verdezoto, N., & Network, C. (2023). Digital Health Technologies for Maternal and Child Health in Africa and Other Low- and Middle-Income Countries: Cross-disciplinary Scoping Review With Stakeholder Consultation. Journal of Medical Internet Research, 25. https://doi.org/10.2196/42161
Turkmani, S., Smith, R., Tan, A., Kamkong, C. B., Anderson, R., Sakulku, S., Jat, T. R., Biswas, A., & Homer, C. (2023). An evaluation of the introduction of telehealth for remote antenatal and postnatal contacts in Bangladesh and Lao People’s Democratic Republic during the COVID-19 pandemic. PLOS Global Public Health, 3(5). https://doi.org/10.1371/journal.pgph.0000786
Umar, M., Shamin, L., Rehman, A., Zafar, H., Talha, M., Tahir, O., Zafar, M., Alam, U., Shams, F., Rahmat, Z. S., & Baig, M. M. A. (2026). The Role of Artificial Intelligence in Enhancing Maternal and Child Health Through Digital Health Initiatives in Resource-Limited Settings: A Narrative Review. Premier Journal of Artificial Intelligence. https://doi.org/10.70389/pjai.100021
Wu, H., Sun, W., Huang, X., Yu, S., Wang, H., Bi, X., Sheng, J., Chen, S., Akinwunmi, B., Zhang, C. J. P., & Ming, W. (2020). Online Antenatal Care During the COVID-19 Pandemic: Opportunities and Challenges. Journal of Medical Internet Research, 22(7). https://doi.org/10.2196/19916
Zenitasari, S., Ganap, E. P., & Rochmah, M. A. (2026). Advancing Maternal Outcomes with E-Health Interventions: A Scoping Review. Universal Journal of Public Health, 14(2), 239–250. https://doi.org/10.13189/ujph.2026.140204
Zhuang, H., Zakama, A., Heller, K., Faulkner, S., Gollub, B., Young-Lin, N., Chen, I. Y., & Asiedu, M. (2025). Large scale analysis and prediction of adverse maternal health events across heterogeneous populations from the All of Us dataset. In Research Square. https://doi.org/10.21203/rs.3.rs-7105527/v1
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nathaniel Gyimah (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
