cirpublications.com-Pnzm-1790899041-d807010582 1790899041 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2025 5 2 Patrick O’Connor Sean Murphy 2025 10.68159/d807010582 https://cirpublications.com/pub/journal/2/article/d807010582 Sibbald L, van den Heuvel MI, Haas MR, van Lissa CJ, van Bakel HJA, Jongerling J, et al. Identifying prenatal risk factors of postpartum depression with machine learning. Sci Rep. 2025;15(1):34610. 10.1038/s41598-025-18204-6 Ricci CA, Crysup B, Phillips NR, Ray WC, Santillan MK, Trask AJ, et al. Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research. Am J Physiol Heart Circ Physiol. 2024;327(2):H417-H432. 10.1152/ajpheart.00149.2024 Vasudevan L, Kibria MG, Kucirka LM, Shieh K, Wei M, Masoumi S, et al. Machine learning models to predict risk of maternal morbidity and mortality from electronic medical record data: scoping review. J Med Internet Res. 2025;27:e68225. 10.2196/68225 Al Mashrafi SS, Tafakori L, Abdollahian M. Predicting maternal risk level using machine learning models. BMC Pregnancy Childbirth. 2024;24(1):820. 10.1186/s12884-024-07030-9 Mapari SA, Shrivastava D, Dave A, Bedi GN, Gupta A, Sachani P, et al. Revolutionizing maternal health: the role of artificial intelligence in enhancing care and accessibility. Cureus. 2024;16(9):e69555. 10.7759/cureus.69555 Chen Y, Huang X, Wu S, Guo P, Huang J, Zhou L, et al. Machine-learning predictive model of pregnancy-induced hypertension in the first trimester. Hypertens Res. 2023;46(9):2135-44. 10.1038/s41440-023-01298-8 Ramakrishnan R, Rao S, He JR. Perinatal health predictors using artificial intelligence: a review. Womens Health (Lond). 2021;17:17455065211046132. 10.1177/17455065211046132 Macrohon JJE, Villavicencio CN, Inbaraj XA, Jeng JH. A semi-supervised machine learning approach in predicting high-risk pregnancies in the Philippines. Diagnostics (Basel). 2022;12(11):2782. 10.3390/diagnostics12112782 El Arab RA, Al Moosa OA, Albahrani Z, Alkhalil I, Somerville J, Abuadas F. Integrating artificial intelligence into perinatal care pathways: a scoping review of reviews of applications, outcomes, and equity. Nurs Rep. 2025;15(8):281. 10.3390/nursrep15080281 Koivu A, Sairanen M, Airola A, Pahikkala T, Leung WC, Lo TK, et al. Adaptive risk prediction system with incremental and transfer learning. Comput Biol Med. 2021;138:104886. 10.1016/j.compbiomed.2021.104886 Ranjbar A, Taeidi E, Mehrnoush V, Roozbeh N, Darsareh F. Machine learning models for predicting pre-eclampsia: a systematic review protocol. BMJ Open. 2023;13(9):e074705. 10.1136/bmjopen-2023-074705 Koivu A, Korpimäki T, Kivelä P, Pahikkala T, Sairanen M. Evaluation of machine learning algorithms for improved risk assessment for Down’s syndrome. Comput Biol Med. 2018;98:1-7. 10.1016/j.compbiomed.2018.05.004 Hoffman MK, Ma N, Roberts A. A machine learning algorithm for predicting maternal readmission for hypertensive disorders of pregnancy. Am J Obstet Gynecol MFM. 2021;3(1):100250. 10.1016/j.ajogmf.2020.100250 Schmidt LJ, Rieger O, Neznansky M, Hackelöer M, Dröge LA, Henrich W, et al. A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes. Am J Obstet Gynecol. 2022;227(1):77.e1-77.e30. 10.1016/j.ajog.2022.01.026 Togunwa TO, Babatunde AO, Abdullah KU. Deep hybrid model for maternal health risk classification in pregnancy: synergy of ANN and random forest. Front Artif Intell. 2023;6:1213436. 10.3389/frai.2023.1213436 Shara N, Mirabal-Beltran R, Talmadge B, Falah N, Ahmad M, Dempers R, et al. Use of machine learning for early detection of maternal cardiovascular conditions: retrospective study using electronic health record data. JMIR Cardio. 2024;8:e53091. 10.2196/53091 Aga MA. Predicting stillbirth and identifying key maternal risk factors using machine learning. BMJ Paediatr Open. 2025;9(1):e004000. 10.1136/bmjpo-2025-004000 Boujarzadeh B, Ranjbar A, Banihashemi F, Mehrnoush V, Darsareh F, Saffari M. Machine learning approach to predict postpartum haemorrhage: a systematic review protocol. BMJ Open. 2023;13(1):e067661. 10.1136/bmjopen-2022-067661 Mwaura HM, Kamanu TK, Kulohoma BW. Bridging data gaps: predicting sub-national maternal mortality rates in Kenya using machine learning models. Cureus. 2024;16(10):e72476. 10.7759/cureus.72476 Jeddi Z, Gryech I, Ghogho M, El Hammoumi M, Mahraoui C. Machine learning for predicting the risk for childhood asthma using prenatal, perinatal, postnatal and environmental factors. Healthcare (Basel). 2021;9(11):1464. 10.3390/healthcare9111464 Cibralic S, Pickup W, Diaz AM, Kohlhoff J, Karlov L, Stylianakis A, et al. The impact of midwifery continuity of care on maternal mental health: a narrative systematic review. Midwifery. 2023;116:103546. 10.1016/j.midw.2022.103546 Sandall J, Fernandez Turienzo C, Devane D, Soltani H, Gillespie P, Gates S, et al. Midwife continuity of care models versus other models of care for childbearing women. Cochrane Database Syst Rev. 2024;4(4):CD004667. 10.1002/14651858.CD004667.pub6 Psaila KM, Schmied V, Heath S. Exploring continuity of care for women with prenatal diagnosis of congenital anomaly: a mixed method study. J Clin Nurs. 2023;32(19-20):7147-61. 10.1111/jocn.16777 D’haenens F, Van Rompaey B, Swinnen E, Dilles T, Beeckman K. The effects of continuity of care on the health of mother and child in the postnatal period: a systematic review. Eur J Public Health. 2020;30(4):749-60. Fernandez Turienzo C, Bick D, Briley AL, Bollard M, Coxon K, Cross P, et al. Midwifery continuity of care versus standard maternity care for women at increased risk of preterm birth: a randomized controlled pilot trial. PLoS Med. 2020;17(10):e1003350. 10.1371/journal.pmed.1003350 Rayment-Jones H, Dalrymple K, Harris J, Harden A, Parslow E, Georgi T, et al. Project20: does continuity of care improve maternal and neonatal outcomes? PLoS One. 2021;16(5):e0250947. 10.1371/journal.pone.0250947 Kallas KA, Marr K, Moirangthem S, Heude B, Koehl M, van der Waerden J, et al. Maternal mental health care matters: impact of prenatal symptoms on child outcomes. J Clin Med. 2023;12(3):1120. 10.3390/jcm12031120 Grande LA, Swales DA, Sandman CA, Glynn LM, Davis EP. Maternal caregiving ameliorates consequences of prenatal distress on child development. Dev Psychopathol. 2022;34(4):1376-85. 10.1017/S0954579421000286 Dalrymple KV, Tydeman F, Bone JF, Poston L, Dasgupta T, McGreevy A, et al. Relationship between virtual antenatal care and pregnancy outcomes. Am J Obstet Gynecol. 2025;233(6):675.e1-675.e36. 10.1016/j.ajog.2025.08.004