{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T11:11:43Z","timestamp":1785841903905,"version":"3.56.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-02921-z","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T15:10:02Z","timestamp":1739373002000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda"],"prefix":"10.1186","volume":"25","author":[{"given":"Muzungu Hirwa","family":"Sylvain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel Christian","family":"Nyabyenda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa","family":"Uwase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac","family":"Komezusenge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fauste","family":"Ndikumana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Innocent","family":"Ngaruye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"2921_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.worlddev.2016.11.013","volume":"92","author":"P Abbott","year":"2017","unstructured":"Abbott P, Sapsford R, Binagwaho A. Learning from Success: how Rwanda achieved the Millennium Development Goals for Health. World Dev. 2017;92:103\u201316.","journal-title":"World Dev"},{"key":"2921_CR2","unstructured":"National Institute of Statistics of Rwanda II. Rwanda Demographic and Health Survey 2020. 2020."},{"key":"2921_CR3","volume-title":"[Rwanda], Ministry of Health (MOH) [Rwanda], ICF. Rwanda Demographic and Health Survey 2019-20 final report. Kigali, Rwanda, and Rockville","author":"National Institute of Statistics of Rwanda (NISR)","year":"2021","unstructured":"National Institute of Statistics of Rwanda (NISR). [Rwanda], Ministry of Health (MOH) [Rwanda], ICF. Rwanda Demographic and Health Survey 2019-20 final report. Kigali, Rwanda, and Rockville. Maryland, USA: NISR and ICF; 2021."},{"key":"2921_CR4","doi-asserted-by":"publisher","first-page":"e195","DOI":"10.1016\/S2214-109X(21)00515-5","volume":"10","author":"D Sharrow","year":"2022","unstructured":"Sharrow D, Hug L, You D, Alkema L, Black R, Cousens S, et al. Global, regional, and national trends in under-5 mortality between 1990 and 2019 with scenario-based projections until 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. Lancet Glob Health. 2022;10:e195\u2013206.","journal-title":"Lancet Glob Health"},{"key":"2921_CR5","volume-title":"Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA\/Population Division","author":"J Cresswell","year":"2023","unstructured":"Cresswell J. Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA\/Population Division. 1st ed. Geneva: World Health Organization; 2023.","edition":"1"},{"key":"2921_CR6","unstructured":"Government of Rwanda. Vision 2050. 2015."},{"key":"2921_CR7","unstructured":"Ministry of Health. Rwanda. Fourth Health Sector Strategic Plan. 2018."},{"key":"2921_CR8","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.healthpol.2010.09.009","volume":"99","author":"P Saksena","year":"2011","unstructured":"Saksena P, Antunes AF, Xu K, Musango L, Carrin G. Mutual health insurance in Rwanda: evidence on access to care and financial risk protection. Health Policy. 2011;99:203\u20139.","journal-title":"Health Policy"},{"key":"2921_CR9","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1186\/s12884-017-1581-4","volume":"17","author":"F Sayinzoga","year":"2017","unstructured":"Sayinzoga F, Bijlmakers L, Van Der Velden K, Van Dillen J. Severe maternal outcomes and quality of care at district hospitals in Rwanda\u2013 a multicentre prospective case-control study. BMC Pregnancy Childbirth. 2017;17:394.","journal-title":"BMC Pregnancy Childbirth"},{"key":"2921_CR10","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s12913-018-2939-7","volume":"18","author":"A Manzi","year":"2018","unstructured":"Manzi A, Nyirazinyoye L, Ntaganira J, Magge H, Bigirimana E, Mukanzabikeshimana L, et al. Beyond coverage: improving the quality of antenatal care delivery through integrated mentorship and quality improvement at health centers in rural Rwanda. BMC Health Serv Res. 2018;18:136.","journal-title":"BMC Health Serv Res"},{"key":"2921_CR11","doi-asserted-by":"publisher","first-page":"e009734","DOI":"10.1136\/bmjopen-2015-009734","volume":"6","author":"F Sayinzoga","year":"2016","unstructured":"Sayinzoga F, Bijlmakers L, Van Dillen J, Mivumbi V, Ngabo F, Van Der Velden K. Maternal death audit in Rwanda 2009\u20132013: a nationwide facility-based retrospective cohort study. BMJ Open. 2016;6:e009734.","journal-title":"BMJ Open"},{"key":"2921_CR12","unstructured":"Ministry of Health, Rwanda. Health Sector Situation Analysis, 2019\u201324. 2024."},{"key":"2921_CR13","unstructured":"Ngabo F, Nguimfack J, Nwaigwe F, Mugeni C, Muhoza D, Wilson DR et al. Designing and implementing an innovative SMS-based alert system (RapidSMS-MCH) to monitor pregnancy and reduce maternal and child deaths in Rwanda. Pan Afr Med J. 2012;13."},{"key":"2921_CR14","first-page":"1","volume-title":"2024 IEEE SmartBlock4Africa","author":"B Ngizimana","year":"2024","unstructured":"Ngizimana B, Nizeyimana P. Clustering and Analysis of Maternal Mortality Causes in Rwanda: implications for targeted Health interventions. In: Proceedings of the 2024 IEEE SmartBlock4Africa. Accra, Ghana: IEEE; 2024. p. 1\u201310."},{"key":"2921_CR15","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s12884-022-04393-9","volume":"22","author":"A Nishimwe","year":"2022","unstructured":"Nishimwe A, Ibisomi L, Nyssen M, Conco DN. The effect of a decision-support mHealth application on maternal and neonatal outcomes in two district hospitals in Rwanda: pre\u2013 post intervention study. BMC Pregnancy Childbirth. 2022;22:52.","journal-title":"BMC Pregnancy Childbirth"},{"key":"2921_CR16","unstructured":"Ministry of Health, Rwanda. The National Digital Health Strategic Plan 2018\u20132023. 2018."},{"key":"2921_CR17","doi-asserted-by":"publisher","unstructured":"Seymour RP, Tang A, DeRiggi J, Munyaburanga C, Cuckovitch R, Nyirishema P, et al. Training software developers for electronic medical records in Rwanda. In: Safran C, Reti S, Marin HF, editors. Proceedings of the 13th World Congress on Medical Informatics (MEDINFO 2010). Studies in Health Technology and Informatics, Vol. 160. Amsterdam: IOS Press; 2010. p. 585\u20139. https:\/\/doi.org\/10.3233\/978-1-60750-588-4-585.","DOI":"10.3233\/978-1-60750-588-4-585"},{"key":"2921_CR18","doi-asserted-by":"publisher","first-page":"22679","DOI":"10.1038\/s41598-024-74536-9","volume":"14","author":"AO Khadidos","year":"2024","unstructured":"Khadidos AO, Saleem F, Selvarajan S, Ullah Z, Khadidos AO. Author correction: Ensemble machine learning framework for predicting maternal health risk during pregnancy. Sci Rep. 2024;14:22679.","journal-title":"Sci Rep"},{"key":"2921_CR19","doi-asserted-by":"publisher","first-page":"780389","DOI":"10.3389\/fbioe.2021.780389","volume":"9","author":"A Bertini","year":"2022","unstructured":"Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F. Using machine learning to predict complications in pregnancy: a systematic review. Front Bioeng Biotechnol. 2022;9:780389.","journal-title":"Front Bioeng Biotechnol"},{"key":"2921_CR20","doi-asserted-by":"publisher","first-page":"975","DOI":"10.3390\/jpm13060975","volume":"13","author":"G Kopanitsa","year":"2023","unstructured":"Kopanitsa G, Metsker O, Kovalchuk S. Machine learning methods for pregnancy and childbirth risk management. J Pers Med. 2023;13:975.","journal-title":"J Pers Med"},{"key":"2921_CR21","doi-asserted-by":"publisher","first-page":"2250045","DOI":"10.1142\/S0219467822500450","volume":"22","author":"S Ravikumar","year":"2022","unstructured":"Ravikumar S, Kannan E. Machine learning techniques for identifying fetal risk during pregnancy. Int J Image Graph. 2022;22:2250045.","journal-title":"Int J Image Graph"},{"key":"2921_CR22","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1186\/s12884-022-04594-2","volume":"22","author":"MN Islam","year":"2022","unstructured":"Islam MN, Mustafina SN, Mahmud T, Khan NI. Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda. BMC Pregnancy Childbirth. 2022;22:348.","journal-title":"BMC Pregnancy Childbirth"},{"key":"2921_CR23","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/bdcc7010032","volume":"7","author":"SS Aljameel","year":"2023","unstructured":"Aljameel SS, Alzahrani M, Almusharraf R, Altukhais M, Alshaia S, Sahlouli H, et al. Prediction of Preeclampsia using Machine Learning and Deep Learning models: a review. Big Data Cogn Comput. 2023;7:32.","journal-title":"Big Data Cogn Comput"},{"key":"2921_CR24","doi-asserted-by":"publisher","first-page":"102710","DOI":"10.1016\/j.ebiom.2020.102710","volume":"54","author":"H Sufriyana","year":"2020","unstructured":"Sufriyana H, Wu Y-W, Su EC-Y. Artificial intelligence-assisted prediction of preeclampsia: development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia. EBioMedicine. 2020;54:102710.","journal-title":"EBioMedicine"},{"key":"2921_CR25","doi-asserted-by":"publisher","first-page":"15793","DOI":"10.1038\/s41598-022-15391-4","volume":"12","author":"SM Lee","year":"2022","unstructured":"Lee SM, Nam Y, Choi ES, Jung YM, Sriram V, Leiby JS, et al. Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning. Sci Rep. 2022;12:15793.","journal-title":"Sci Rep"},{"key":"2921_CR26","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s11517-023-02800-7","volume":"61","author":"B Kurt","year":"2023","unstructured":"Kurt B, G\u00fcrlek B, Keskin S, \u00d6zdemir S, Karadeniz \u00d6, K\u0131rkbir \u0130B, et al. Prediction of gestational diabetes using deep learning and bayesian optimization and traditional machine learning techniques. Med Biol Eng Comput. 2023;61:1649\u201360.","journal-title":"Med Biol Eng Comput"},{"key":"2921_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6665573","volume":"2021","author":"R Raja","year":"2021","unstructured":"Raja R, Mukherjee I, Sarkar BK. A machine learning-based prediction model for Preterm Birth in Rural India. J Healthc Eng. 2021;2021:1\u201311.","journal-title":"J Healthc Eng"},{"key":"2921_CR28","doi-asserted-by":"publisher","first-page":"e0273178","DOI":"10.1371\/journal.pone.0273178","volume":"17","author":"SA Shazly","year":"2022","unstructured":"Shazly SA, Borah BJ, Ngufor CG, Torbenson VE, Theiler RN, Famuyide AO. Impact of labor characteristics on maternal and neonatal outcomes of labor: a machine-learning model. PLoS ONE. 2022;17:e0273178.","journal-title":"PLoS ONE"},{"key":"2921_CR29","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1186\/s12884-021-04087-8","volume":"21","author":"G Amit","year":"2021","unstructured":"Amit G, Girshovitz I, Marcus K, Zhang Y, Pathak J, Bar V, et al. Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy Childbirth. 2021;21:630.","journal-title":"BMC Pregnancy Childbirth"},{"key":"2921_CR30","doi-asserted-by":"publisher","first-page":"e0312447","DOI":"10.1371\/journal.pone.0312447","volume":"19","author":"T Kubahoniyesu","year":"2024","unstructured":"Kubahoniyesu T, Kabano IH. Predicting adverse pregnancy outcome in Rwanda using machine learning techniques. PLoS ONE. 2024;19:e0312447.","journal-title":"PLoS ONE"},{"key":"2921_CR31","doi-asserted-by":"crossref","unstructured":"Uwambajimana E, Rugirangoga P, Musabyimana E, Ingabire N, Ikibasumba J, Turikumwenimana R et al. Assessment of the use of electronic medical records system and barriers in Rwanda. 2024.","DOI":"10.21203\/rs.3.rs-4763866\/v1"},{"key":"2921_CR32","unstructured":"National Institute of Statistics of Rwanda. Rwanda Statistical Yearbook 2023. National Institute of Statistics of Rwanda; 2023."},{"key":"2921_CR33","unstructured":"Van Rossum G. Python programming language. In: USENIX annual technical conference. Santa Clara, CA; 2007. pp. 1\u201336."},{"key":"2921_CR34","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2307\/1966825","volume":"23","author":"J McCarthy","year":"1992","unstructured":"McCarthy J, Maine D. A Framework for analyzing the determinants of maternal mortality. Stud Fam Plann. 1992;23:23.","journal-title":"Stud Fam Plann"},{"key":"2921_CR35","unstructured":"Joel LO, Doorsamy W, Paul BS. On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets. 2024."},{"key":"2921_CR36","doi-asserted-by":"crossref","unstructured":"Hang Y, Zhang Y, Lv Y, Yu W, Lin Y. Electronic medical record based machine learning methods for adverse pregnancy outcome prediction. In: Tian J, Mao K, Qian D, Xie Y, Lyu Y, editors. Twelfth International Conference on Signal Processing Systems. Shanghai, China: SPIE; 2021. p. 17.","DOI":"10.1117\/12.2581720"},{"key":"2921_CR37","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy D, Atiya AF. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci. 2019;505:32\u201364.","journal-title":"Inf Sci"},{"key":"2921_CR38","first-page":"338","volume":"38","author":"ME Edmondson","year":"2020","unstructured":"Edmondson ME, Reimer AP. Challenges frequently encountered in the secondary use of Electronic Medical Record Data for Research. CIN Comput Inf Nurs. 2020;38:338\u201348.","journal-title":"CIN Comput Inf Nurs"},{"key":"2921_CR39","doi-asserted-by":"publisher","first-page":"014001","DOI":"10.1088\/2057-1739\/aaa905","volume":"4","author":"W-W Yim","year":"2018","unstructured":"Yim W-W, Wheeler AJ, Curtin C, Wagner TH, Hernandez-Boussard T. Secondary use of electronic medical records for clinical research: challenges and opportunities. Converg Sci Phys Oncol. 2018;4:014001.","journal-title":"Converg Sci Phys Oncol"},{"key":"2921_CR40","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1093\/jamia\/ocw042","volume":"24","author":"BA Goldstein","year":"2017","unstructured":"Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inf Assoc. 2017;24:198\u2013208.","journal-title":"J Am Med Inf Assoc"},{"key":"2921_CR41","doi-asserted-by":"publisher","first-page":"e49127","DOI":"10.2196\/49127","volume":"10","author":"HSF Fraser","year":"2024","unstructured":"Fraser HSF, Mugisha M, Bacher I, Ngenzi JL, Seebregts C, Umubyeyi A, et al. Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the role of clinical Alerts: cross-sectional observational study. JMIR Public Health Surveill. 2024;10:e49127.","journal-title":"JMIR Public Health Surveill"},{"key":"2921_CR42","unstructured":"Amoroso CL, Akimana B, Wise B, Fraser HS. Using electronic medical records for HIV care in rural Rwanda. In: Safran C, Reti S, Marin HF, editors. Proceedings of the 13th World Congress on Medical Informatics (MEDINFO 2010). Studies in Health Technology and Informatics, Vol. 160. Amsterdam: IOS Press; 2010. p. 337\u201341."},{"key":"2921_CR43","unstructured":"Anokwa Y, Allen C, Parikh T. Deploying a Medical Record System in Rural Rwanda. HCI Community Int Dev CHI. 2008;2008."},{"key":"2921_CR44","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ajog.2020.10.030","volume":"224","author":"GJ Escobar","year":"2021","unstructured":"Escobar GJ, Soltesz L, Schuler A, Niki H, Malenica I, Lee C. Prediction of obstetrical and fetal complications using automated electronic health record data. Am J Obstet Gynecol. 2021;224:137\u2013e1477.","journal-title":"Am J Obstet Gynecol"},{"key":"2921_CR45","doi-asserted-by":"publisher","first-page":"e16503","DOI":"10.2196\/16503","volume":"8","author":"H Sufriyana","year":"2020","unstructured":"Sufriyana H, Husnayain A, Chen Y-L, Kuo C-Y, Singh O, Yeh T-Y, et al. Comparison of Multivariable Logistic Regression and other machine learning algorithms for prognostic prediction studies in pregnancy care: systematic review and Meta-analysis. JMIR Med Inf. 2020;8:e16503.","journal-title":"JMIR Med Inf"},{"key":"2921_CR46","doi-asserted-by":"crossref","unstructured":"Moreira MWL, Rodrigues JJPC, Oliveira AMB, Saleem K, Venancio Neto AJ. Predicting hypertensive disorders in high-risk pregnancy using the random forest approach. In: 2017 IEEE International Conference on Communications (ICC). Paris: IEEE; 2017. pp. 1\u20135.","DOI":"10.1109\/ICC.2017.7996964"},{"key":"2921_CR47","doi-asserted-by":"publisher","first-page":"418","DOI":"10.3390\/jcm12020418","volume":"12","author":"A-S Melinte-Popescu","year":"2023","unstructured":"Melinte-Popescu A-S, Vasilache I-A, Socolov D, Melinte-Popescu M. Predictive performance of machine learning-based methods for the prediction of Preeclampsia\u2014A prospective study. J Clin Med. 2023;12:418.","journal-title":"J Clin Med"},{"key":"2921_CR48","doi-asserted-by":"publisher","first-page":"106740","DOI":"10.1016\/j.cmpb.2022.106740","volume":"219","author":"A De Ram\u00f3n Fern\u00e1ndez","year":"2022","unstructured":"De Ram\u00f3n Fern\u00e1ndez A, Ruiz Fern\u00e1ndez D, Prieto S\u00e1nchez MT. Prediction of the mode of delivery using artificial intelligence algorithms. Comput Methods Programs Biomed. 2022;219:106740.","journal-title":"Comput Methods Programs Biomed"},{"key":"2921_CR49","doi-asserted-by":"publisher","first-page":"3677","DOI":"10.1080\/14767058.2020.1837769","volume":"35","author":"R Meyer","year":"2022","unstructured":"Meyer R, Hendin N, Zamir M, Mor N, Levin G, Sivan E, et al. Implementation of machine learning models for the prediction of vaginal birth after cesarean delivery. J Matern Fetal Neonatal Med. 2022;35:3677\u201383.","journal-title":"J Matern Fetal Neonatal Med"},{"key":"2921_CR50","doi-asserted-by":"crossref","unstructured":"Despotovic D, Zec A, Mladenovic K, Radin N, Turukalo TL. A Machine Learning Approach for an Early Prediction of Preterm Delivery. In: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY). Subotica: IEEE; 2018. pp. 000265\u201370.","DOI":"10.1109\/SISY.2018.8524818"},{"key":"2921_CR51","unstructured":"National Institute of Statistics of Rwanda (NISR). Fifth Rwanda Population and Housing Census, Thematic Report: Mortality. National Institute of Statistics of Rwanda; 2023."},{"key":"2921_CR52","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1067\/mob.2002.119639","volume":"186","author":"J Bai","year":"2002","unstructured":"Bai J, Wong FWS, Bauman A, Mohsin M. Parity and pregnancy outcomes. Am J Obstet Gynecol. 2002;186:274\u20138.","journal-title":"Am J Obstet Gynecol"},{"key":"2921_CR53","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1186\/1471-2458-7-268","volume":"7","author":"K Raatikainen","year":"2007","unstructured":"Raatikainen K, Heiskanen N, Heinonen S. Under-attending free antenatal care is associated with adverse pregnancy outcomes. BMC Public Health. 2007;7:268.","journal-title":"BMC Public Health"},{"key":"2921_CR54","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1055\/s-0041-1739432","volume":"40","author":"AR Kern-Goldberger","year":"2023","unstructured":"Kern-Goldberger AR, Ewing J, Polin M, D\u2019Alton M, Friedman AM, Goffman D. The predictive value of vital signs for morbidity in pregnancy: evaluating and optimizing maternal early warning systems. Am J Perinatol. 2023;40:1590\u2013601.","journal-title":"Am J Perinatol"},{"key":"2921_CR55","doi-asserted-by":"publisher","first-page":"e0235835","DOI":"10.1371\/journal.pone.0235835","volume":"15","author":"R Ueno","year":"2020","unstructured":"Ueno R, Xu L, Uegami W, Matsui H, Okui J, Hayashi H, et al. Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: a single-center retrospective cohort study. PLoS ONE. 2020;15:e0235835.","journal-title":"PLoS ONE"},{"key":"2921_CR56","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.socscimed.2019.02.040","volume":"226","author":"D Kpienbaareh","year":"2019","unstructured":"Kpienbaareh D, Atuoye KN, Ngabonzima A, Bagambe PG, Rulisa S, Luginaah I, et al. Spatio-temporal disparities in maternal health service utilization in Rwanda: what next for SDGs? Soc Sci Med. 2019;226:164\u201375.","journal-title":"Soc Sci Med"},{"key":"2921_CR57","doi-asserted-by":"publisher","first-page":"307","DOI":"10.4236\/ojog.2013.33057","volume":"03","author":"A Shamsa","year":"2013","unstructured":"Shamsa A, Bai J, Raviraj P, Gyaneshwar R. Mode of delivery and its associated maternal and neonatal outcomes. Open J Obstet Gynecol. 2013;03:307\u201312.","journal-title":"Open J Obstet Gynecol"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-02921-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-02921-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-02921-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T15:10:22Z","timestamp":1739373022000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-02921-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2921"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-02921-z","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,12]]},"assertion":[{"value":"12 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval for this study was obtained from the Institutional Review Board (IRB) of the University of Rwanda with reference number CMHS\/IRB\/338\/2024. All data used in this research were anonymized before analysis to ensure the privacy and confidentiality of participants. Identifiable information such as patient names, national identification numbers, and addresses were removed in compliance with ethical standards. As this study involved secondary data analysis of retrospective electronic medical records, formal consent from participants was waived by the IRB, given that no direct interaction with patients occurred. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and relevant national guidelines.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"76"}}