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Review

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 531-543 | Received 04 Jan 2023, Accepted 06 Jun 2023, Published online: 21 Jun 2023

References

  • Huang C, Wei K, Lee PMY, et al. Maternal hypertensive disorder of pregnancy and mortality in offspring from birth to young adulthood: national population based cohort study. BMJ. 2022 [cited 2023 May 28]. 379:e072157.
  • Chappell LC, Cluver CA, Kingdom J, et al. Pre-eclampsia. Lancet. 2021 398(10297):341–354. [cited 2023 May 28]. doi:10.1016/S0140-6736(20)32335-7.
  • Brown MA, Lindheimer MD, De Swiet M, et al. The classification and diagnosis of the hypertensive disorders of pregnancy: statement from the international society for the study of hypertension in pregnancy (ISSHP). Hypertens Pregnancy. 2001 20(1):ix–xiv. [cited 2023 May 28]. doi:10.1081/PRG-100104165.
  • Say L, Chou D, Gemmill A, et al. Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health. 2014 2(6):e323–e333. [cited 2023 May 28]. doi:10.1016/S2214-109X(14)70227-X.
  • Ahmed R, Dunford J, Mehran R, et al. Pre-eclampsia and future cardiovascular risk among women: a review. J Am Coll Cardiol. 2014 63(18):1815–1822. [cited 2023 May 28]. doi:10.1016/j.jacc.2014.02.529.
  • Tranquilli AL, Dekker G, Magee L, et al. The classification, diagnosis and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP. Pregnancy Hypertens. 2014 4(2):97–104. [cited 2023 May 28]. doi:10.1016/j.preghy.2014.02.001.
  • Wu P, Haththotuwa R, Kwok CS, et al. Preeclampsia and future cardiovascular health: a systematic review and meta-analysis. Circ Cardiovasc Qual Outcomes. 2017 10(2). [cited 2023 May 28]. doi:10.1161/CIRCOUTCOMES.116.003497.
  • Hauspurg A, Ying W, Hubel CA, et al. Adverse pregnancy outcomes and future maternal cardiovascular disease. Clin Cardiol. 2018 41(2):239–246. [cited 2023 May 28]. doi:10.1002/clc.22887.
  • Ying W, Catov JM, Ouyang P. Hypertensive disorders of pregnancy and future maternal cardiovascular risk. J Am Heart Assoc. 2018 7(17). [cited 2023 May 28]. doi: 10.1161/JAHA.118.009382
  • Knight M, Bunch K, Tuffnell D, et al. Saving lives, improving mothers’ care maternal, newborn and infant clinical outcome review programme; 2021. [cited 2023 May 28]. Available from: www.hqip.org.uk/national-programmes.
  • DC G, DM L-J, B G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines. Circulation. 2014 [cited 2023 May 28].129:S49–S73.
  • Vogel B, Acevedo M, Appelman Y, et al. The lancet women and cardiovascular disease commission: reducing the global burden by 2030. Lancet. 2021 397(10292):2385–2438. [cited 2023 May 28]. doi:10.1016/S0140-6736(21)00684-X.
  • O’Brien TE, Ray JG, Chan WS. Maternal body mass index and the risk of preeclampsia: a systematic overview.Epidemiology. 2003 14(3):368–374. [cited 2023 May 28]. doi:10.1097/01.EDE.0000059921.71494.D1.
  • Visintin C, Mugglestone MA, Almerie MQ, et al. Management of hypertensive disorders during pregnancy: summary of NICE guidance. BMJ 2010 341:499–501. [cited 2023 May 28]. doi: 10.1136/bmj.c2207.
  • Haug EB, Horn J, Markovitz AR, et al. Association of conventional cardiovascular risk factors with cardiovascular disease after hypertensive disorders of pregnancy: analysis of the nord-trondelag health study. JAMA Cardiol. 2019 4(7):628–635. [cited 2023 May 28]. doi:10.1001/jamacardio.2019.1746.
  • Lane-Cordova AD, Khan SS, Grobman WA, et al. Long-term cardiovascular risks associated with adverse pregnancy outcomes: JACC review topic of the week. J Am Coll Cardiol. 2019 73(16):2106–2116. [cited 2023 May 28]. doi: 10.1016/j.jacc.2018.12.092.
  • Thong EP, Ghelani DP, Manoleehakul P, et al. Optimising cardiometabolic risk factors in pregnancy: a review of risk prediction models targeting gestational diabetes and hypertensive disorders. J Cardiovasc Dev Dis 2022 9:55. [cited 2023 May 28]. doi:10.3390/jcdd9020055.
  • Wang G, Zhang Y, Li S, et al. A machine learning-based prediction model for cardiovascular risk in women with preeclampsia. Front Cardiovasc Med. 2021 [cited 2023 May 28];8:736491.
  • Yang L, Sun G, Wang A, et al. Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm. Technol Health Care. 2020 [cited 2023 May 28];28:S181–S186.
  • Deo RC. Machine learning in medicine.Circulation. 2015 132(20):1920–1930. [cited 2023 May 28]. doi:10.1161/CIRCULATIONAHA.115.001593.
  • TURING AM. I.—computing machinery and intelligence. Mind. 1950 LIX:433–460 [cited 2023 May 28]. doi: 10.1093/mind/LIX.236.433.
  • Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 69S:S36–S40. [cited 2023 May 28]. doi: 10.1016/j.metabol.2017.01.011.
  • Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021 14(1):86–93. [cited 2023 May 28] doi:10.1111/cts.12884.
  • Schork NJ. Artificial intelligence and personalized medicine. Cancer Treat Res. 2019 178:265–283. [cited 2023 May 28]. Available from https://pubmed.ncbi.nlm.nih.gov/31209850/
  • Padmanabhan S, Tran TQB, Dominiczak AF. Artificial intelligence in hypertension: seeing through a glass darkly.Circ Res. 2021 128(7):1100–1118. [cited 2023 May 28]. doi:10.1161/CIRCRESAHA.121.318106.
  • Bostrom N Nick bostrom - superintelligence_ paths, dangers, strategies (2014, Oxford University Press).Pdf; 2016. [cited 2023 May 28]. Available from: https://global.oup.com/academic/product/superintelligence-9780198739838.
  • Haq IU, Haq I, Xu B. Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging.Cardiovasc Diagn Ther. 2021 11(3):911–923. [cited 2023 May 28]. doi:10.21037/cdt.2020.03.09.
  • Quer G, Arnaout R, Henne M, et al. Machine learning and the future of cardiovascular care: JACC state-of-the-art review. J Am Coll Cardiol. 2021 77(3):300–313. [cited 2023 May 28]. doi:10.1016/j.jacc.2020.11.030.
  • Krittanawong C, Virk HUH, Bangalore S, et al. Machine learning prediction in cardiovascular diseases: a meta-analysis. Sci Rep. 2020 10(1). [cited 2023 May 28]. doi:10.1038/s41598-020-72685-1.
  • De Marvao A, Dawes TJW, Howard JP, et al. Artificial intelligence and the cardiologist: what you need to know for 2020. Heart. 2020106(5):399–400. [cited 2023 May 28]. doi:10.1136/heartjnl-2019-316033.
  • Madani A, Ong JR, Tibrewal A, et al. Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit Med. 2018 [cited 2023 May 28].1. Available from: https://pubmed.ncbi.nlm.nih.gov/31304338/
  • Seetharam K, Raina S, Sengupta PP. The role of artificial intelligence in echocardiography. Curr Cardiol Rep. 2020 22(9). [cited 2023 May 28]. doi: 10.1007/s11886-020-01329-7
  • Hejazi M, Al-Haddad SAR, Singh YP, et al. Multiclass support vector machines for classification of ECG data with missing values. Appl Artif Intell. 2015 29(7):660–674. [cited 2023 May 28]. doi:10.1080/0883951420151051887.
  • Alkhodari M, Jelinek HF, Werghi N, et al. Estimating left ventricle ejection fraction levels using circadian heart rate variability features and support vector regression models. IEEE J Biomed Health Inform. 2021 25(3):746–754. [cited 2023 May 28]. doi:10.1109/JBHI.2020.3002336.
  • Bai Y, Yao H, Jiang X, et al. Construction of a non-mutually exclusive decision tree for medication recommendation of chronic heart failure. Front Pharmacol. 2022 12. [cited 2023 May 28]. doi: 10.3389/fphar.2021.758573
  • Kobayashi M, Huttin O, Magnusson M, et al. Machine learning-derived echocardiographic phenotypes predict heart failure incidence in asymptomatic individuals. JACC Cardiovasc Imaging. 2022;15(2):193–208. doi:10.1016/j.jcmg.2021.07.004
  • Yang L, Wu H, Jin X, et al. Study of cardiovascular disease prediction model based on random forest in eastern china. Sci Rep. 2020 10(1). [cited 2023 May 28]. doi:10.1038/s41598-020-62133-5.
  • Li Y, Wang H, Luo Y. Improving fairness in the prediction of heart failure length of stay and mortality by integrating social determinants of health. Circ Heart Fail. 2022 15:E009473. [cited 2023 May 28]. doi:10.1161/CIRCHEARTFAILURE.122.009473
  • Wang K, Tian J, Zheng C, et al. Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP. Comput Biol Med. 2021 [cited 2023 May 28].137:104813.
  • Wang Y, Miao X, Xiao G, et al. Clinical prediction of heart failure in hemodialysis patients: based on the extreme gradient boosting method. Front Genet. 2022 13. [cited 2023 May 28]. doi: 10.3389/fgene.2022.889378
  • Ricciardi C, Edmunds KJ, Recenti M, et al. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Sci Rep. 2020 10(1). [cited 2023 May 28]. doi:10.1038/s41598-020-59873-9.
  • Jia S, Mou H, Wu Y, et al. A simple logistic regression model for predicting the likelihood of recurrence of atrial fibrillation within 1 year after initial radio-frequency catheter ablation therapy. Front Cardiovasc Med. 2022 8. [cited 2023 May 28]. doi: 10.3389/fcvm.2021.819341
  • Huang W, Ying TW, Chin WLC, et al. Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction. Sci Rep. 2022 12(1). [cited 2023 May 28]. doi:10.1038/s41598-021-04649-y.
  • Peisker F, Halder M, Nagai J, et al. Mapping the cardiac vascular niche in heart failure. Nat Commun. 2022 13(1). [cited 2023 May 28]. doi:10.1038/s41467-022-30682-0.
  • Ahmad T, Lund LH, Rao P, et al. Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc. 2018 7(8). [cited 2023 May 28]. doi:10.1161/JAHA.117.008081.
  • Marchuk Y, Magrans R, Sales B, et al. Predicting patient-ventilator asynchronies with hidden Markov models. Sci Rep. 2018 8(1). [cited 2023 May 28]. doi:10.1038/s41598-018-36011-0.
  • Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020 3. [cited 2023 May 28]. doi: 10.1038/s41746-019-0216-8
  • Chen C, Qin C, Qiu H, et al. Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med. 2020 7. [cited 2023 May 28]. doi: 10.3389/fcvm.2020.00025
  • Bizopoulos P, Koutsouris D. Deep learning in cardiology. IEEE Rev Biomed Eng. 2019 12:168–193. [cited 2023 May 28]. doi:10.1109/RBME.2018.2885714
  • Somani S, Russak AJ, Richter F, et al. Deep learning and the electrocardiogram: review of the current state-of-the-art. EP Europace. 2021 23(8):1179–1191. [cited 2023 May 28]. doi:10.1093/europace/euaa377.
  • Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021 12(1). [cited 2023 May 28]. doi:10.1038/s41467-021-20966-2.
  • You H, Bae EK, Moon Y, et al. Automatic control of cardiac ablation catheter with deep reinforcement learning method. J Mech Sci Technol. 2019 33(11):5415–5423. [cited 2023 May 28]. doi:10.1007/s12206-019-1036-0.
  • Nanayakkara Id T, Clermont G, Langmead CJ, et al. Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. PLOS Digital Health. 2022 [cited 2023 May 28];1:e0000012.
  • Su YH, Huang K, Hannaford B. Multicamera 3D viewpoint adjustment for robotic surgery via deep reinforcement learning. 2021 6. [cited 2023 May 28]. doi:10.1001/jamanetworkopen.2023.3367
  • Liu S, See KC, Ngiam KY, et al. Reinforcement learning for clinical decision support in critical care: comprehensive review. J Med Internet Res. 2020 22(7):e18477. [cited 2023 May 28]. doi:10.2196/18477.
  • Subramanian M, Wojtusciszyn A, Favre L, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med. 2020 18(1). [cited 2023 May 28]. doi:10.1186/s12967-020-02658-5.
  • Kamel Boulos MN, Zhang P. Digital twins: from personalised medicine to precision public health.J Pers Med. 2021 11(8):745. [cited 2023 May 28]. doi:10.3390/jpm11080745.
  • Uddin M, Wang Y, Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. NPJ Digit Med. 2019 2. [cited 2023 May 28]. doi:10.1038/s41746-019-0191-0
  • Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018 71(23):2668–2679. [cited 2023 May 28]. doi:10.1016/j.jacc.2018.03.521.
  • Steinhubl SR, Topol EJ. Moving from digitalization to digitization in cardiovascular care: why is it important, and what could it mean for patients and providers?J Am Coll Cardiol. 2015 66(13):1489–1496. [cited 2023 May 28]. doi:10.1016/j.jacc.2015.08.006.
  • Kirchhof P, Sipido KR, Cowie MR, et al. The continuum of personalized cardiovascular medicine: a position paper of the European Society of Cardiology. Eur Heart J. 2014 35(46):3250–3257. [cited 2023 May 28]. doi:10.1093/eurheartj/ehu312.
  • Weng SF, Reps J, Kai J, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE. 2017 12(4):e0174944. [cited 2023 May 28]. doi:10.1371/journal.pone.0174944.
  • Westerlund AM, Hawe JS, Heinig M, et al. Risk prediction of cardiovascular events by exploration of molecular data with explainable artificial intelligence. Int J Mol Sci. 2021 22(19):10291. [cited 2023 May 28]. doi:10.3390/ijms221910291.
  • Patel B, Sengupta P. Machine learning for predicting cardiac events: what does the future hold?Expert Rev Cardiovasc Ther. 2020 18(2):77–84. [cited 2023 May 28]. doi:10.1080/14779072.2020.1732208.
  • D’Agostino RB, Pencina MJ, Massaro JM, et al. Cardiovascular disease risk assessment: insights from Framingham. Glob Heart. 2013 [cited 2023 May 28];8:11–23.
  • Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017 357:j2099. [cited 2023 May 28]. doi:10.1136/bmj.j2099
  • Zhao D, Liu J, Xie W, et al. Cardiovascular risk assessment: a global perspective. Nat Rev Cardiol. 2015 12(5):301–311. [cited 2023 May 28]. doi:10.1038/nrcardio.2015.28.
  • Truslow JG, Goto S, Homilius M, et al. Cardiovascular risk assessment using artificial intelligence-enabled event adjudication and hematologic predictors. Circ Cardiovasc Qual Outcomes. 2022 15(6):E008007. [cited 2023 May 28]. doi:10.1161/CIRCOUTCOMES.121.008007.
  • Siva Kumar S, Al-Kindi S, Tashtish N, et al. Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring. Front Cardiovasc Med. 2022 9. [cited 2023 May 28]. doi: 10.3389/fcvm.2022.976769
  • Nakanishi R, Slomka PJ, Rios R, et al. Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths. JACC Cardiovasc Imaging. 2021 14(3):615–625. [cited 2023 May 28]. doi:10.1016/j.jcmg.2020.08.024.
  • Cho SY, Kim SH, Kang SH, et al. Pre-existing and machine learning-based models for cardiovascular risk prediction. Sci Rep. 2021 11(1):8886. [cited 2023 May 28]. doi:10.1038/s41598-021-88257-w.
  • Rudnicka AR, Welikala R, Barman S, et al. Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke. Br J Ophthalmol. 2022 106(12):1722–1729. [cited 2023 May 28]. doi:10.1136/bjo-2022-321842.
  • Cornia PB, Johnson KM, Jackson MB. Cardiac risk stratification. The perioperative medicine consult handbook: third edition [internet]. 2022 45–57. [cited 2023 May 28]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK507785/
  • Sharifi M, Rakhit RD, Humphries SE, et al. Cardiovascular risk stratification in familial hypercholesterolaemia. Heart. 2016 102(13):1003–1008. [cited 2023 May 28]. doi:10.1136/heartjnl-2015-308845.
  • Suri JS, Bhagawati M, Paul S, et al. A powerful paradigm for cardiovascular risk stratification using multiclass, multi-label, and ensemble-based machine learning paradigms: a narrative review. Diagnostics. 2022 12(3):722. [cited 2023 May 28]. doi:10.3390/diagnostics12030722.
  • Rim TH, Lee CJ, Tham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021 3: [cited 2023 May 28]. e306–e316.
  • Sarraju A, Ward A, Chung S, et al. Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients. Open Heart. 2021 8(2):e001802. [cited 2023 May 28]. doi:10.1136/openhrt-2021-001802.
  • Coorey G, Figtree GA, Fletcher DF, et al. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med. 2022 5. [cited 2023 May 28]. doi: 10.1038/s41746-022-00640-7
  • Tao F, Zhang H, Liu A, et al. Digital twin in industry: state-of-the-art. IEEE Trans Industr Inform. 2019;15(4):2405–2415. doi:10.1109/TII.2018.2873186
  • Venkatesh KP, Raza MM, Kvedar JC. Health digital twins as tools for precision medicine: considerations for computation, implementation, and regulation. NPJ Digit Med. 2022 5. [cited 2023 May 28]. doi: 10.1038/s41746-022-00694-7
  • Croatti A, Gabellini M, Montagna S, et al. On the integration of agents and digital twins in healthcare. J Med Syst. 2020 44(9). [cited 2023 May 28]. doi:10.1007/s10916-020-01623-5.
  • Tranvåg EJ, Strand R, Ottersen T, et al. Precision medicine and the principle of equal treatment: a conjoint analysis. BMC Med Ethics. 2021 22(1). [cited 2023 May 28]. doi:10.1186/s12910-021-00625-3.
  • Leopold JA, Loscalzo J. Emerging role of precision medicine in cardiovascular disease.Circ Res. 2018 122(9):1302–1315. [cited 2023 May 28]. doi:10.1161/CIRCRESAHA.117.310782.
  • Dilsizian ME, Siegel EL. Machine meets biology: a primer on artificial intelligence in cardiology and cardiac imaging. Curr Cardiol Rep. 2018 20(12). [cited 2023 May 28]. doi: 10.1007/s11886-018-1074-8
  • Barricelli BR, Casiraghi E, Fogli D. A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access. 2019 7:167653–167671. [cited 2023 May 28]. doi:10.1109/ACCESS.2019.2953499
  • Chakshu NK, Sazonov I, Nithiarasu P. Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis.Biomech Model Mechanobiol. 2021 20(2):449–465. [cited 2023 May 28]. doi:10.1007/s10237-020-01393-6.
  • Semakova A, Zvartau N. Data-driven identification of hypertensive patient profiles for patient population simulation. Procedia Comput Sci. 2018;136:433–442. doi: 10.1016/j.procs.2018.08.269
  • Vukicevic M, Vekilov DP, Grande-Allen JK, et al. Patient-specific 3D valve modeling for structural intervention. new pub: elsevier. Structural Heart. 2017 1(5–6):236–248. [cited 2023 May 28]. doi:10.1080/24748706.2017.1377363.
  • Naplekov I, Zheleznikov I, Pashchenko D, et al. Methods of computational modeling of coronary heart vessels for its digital twin. MATEC Web of Conferences. 2018 cited 2023 May 28;172:p. 01009. Available from: https://www.matec-conferences.org/articles/matecconf/abs/2018/31/matecconf_icdams2018_01009/matecconf_icdams2018_01009.html.
  • Mazumder O, Roy D, Bhattacharya S, et al. Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a digital twin of cardiovascular system. Annu Int Conf IEEE Eng Med Biol Soc. 2019 [cited 2023 May 28];2019:5024–5029.
  • Ravi D, Wong C, Deligianni F, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017 21(1):4–21. [cited 2023 May 28]. doi:10.1109/JBHI.2016.2636665.
  • Xing X, Del Ser J, Wu Y, et al. HDL: hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis. IEEE J Biomed Health Inform. 2022 1–1. [cited 2023 May 28]. doi:10.1109/JBHI.2022.3158897
  • Allen NB, Krefman AE, Labarthe D, et al. Cardiovascular health trajectories from childhood through middle age and their association with subclinical atherosclerosis. JAMA Cardiol. 2020 5(5):557–566. [cited 2023 May 28]. doi:10.1001/jamacardio.2020.0140.
  • Thorsen-Meyer HC, Nielsen AB, Nielsen AP, et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digit Health. 2020 [cited 2023 May 28];2:e179–e191.
  • Siggaard T, Reguant R, Jørgensen IF, et al. Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. Nat Commun. 2020 11(1). [cited 2023 May 28]. doi:10.1038/s41467-020-18682-4.
  • Allam A, Feuerriegel S, Rebhan M, et al. Analyzing patient trajectories with artificial intelligence. J Med Internet Res. 2021 23(12):e29812. [cited 2023 May 28]. doi:10.2196/29812.
  • Zhao J, Feng QP, Wu P, et al. Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Sci Rep. 2019 9(1). [cited 2023 May 28]. doi:10.1038/s41598-018-36745-x.
  • Haug N, Deischinger C, Gyimesi M, et al. High-risk multimorbidity patterns on the road to cardiovascular mortality. BMC Med. 2020 18(1). [cited 2023 May 28]. doi:10.1186/s12916-020-1508-1.
  • Lu XH, Liu A, Fuh SC, et al. Recurrent disease progression networks for modelling risk trajectory of heart failure. PLoS ONE. 2021 16(1):e0245177. [cited 2023 May 28]. doi:10.1371/journal.pone.0245177.
  • Tavazzi E, Daberdaku S, Zandonà A, et al. Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression. J Neurol. 2022 269(7):3858–3878. [cited 2023 May 28]. doi:10.1007/s00415-022-11022-0.
  • Wesołowski S, Lemmon G, Hernandez EJ, et al. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS Digital Health. 2022 [cited 2023 May 28];1:e0000004.
  • Yu J, Flatley C, Greer RM, et al. Birth-weight centiles and the risk of serious adverse neonatal outcomes at term. J Perinat Med. 2018 46(9):1048–1056. [cited 2023 May 28]. doi:10.1515/jpm-2017-0176.
  • Jhee JH, Lee S, Park Y, et al. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS ONE. 2019 14(8):e0221202. [cited 2023 May 28]. doi:10.1371/journal.pone.0221202.
  • Sufriyana H, Wu YW, Su ECY. 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. [cited 2023 May 28]. doi:10.1016/j.ebiom.2020.102710
  • Liu M, Yang X, Chen G, et al. Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in china. Front Physiol. 2022 13. [cited 2023 May 28]. doi:10.3389/fphys.2022.896969
  • Lee SM, Nam Y, Choi ES, et al. Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning. Sci Rep. 2022;12:1–13. doi:10.1038/s41598-022-15391-4
  • Ghaemi MS, DiGiulio DB, Contrepois K, et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics. 2019 35(1):95–103. [cited 2023 May 28]. doi:10.1093/bioinformatics/bty537.
  • Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol. 2023 14. [cited 2023 May 28]. doi:10.3389/fendo.2023.1081667
  • Wang H, Zhang Z, Li H, et al. A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery. Cell Biosci. 2023 13(1). [cited 2023 May 28]. doi:10.1186/s13578-023-00991-y.
  • Lundberg SM, Allen PG, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017 [cited 2023 May 28]. 30:1–10.
  • Varghese B, Jala A, Meka S, et al. Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy. Am J Obstet Gynecol MFM. 2023 5(2):100829. [cited 2023 May 28]. doi:10.1016/j.ajogmf.2022.100829.
  • Marić I, Contrepois K, Moufarrej MN, et al. Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns (N Y). 2022 3(12):100655. [cited 2023 May 28]. doi:10.1016/j.patter.2022.100655.
  • Mureddu GF. How much does hypertension in pregnancy affect the risk of future cardiovascular events?Eur Heart J Suppl. 2023 25(Supplement_B):B111–B113. [cited 2023 May 28]. doi:10.1093/eurheartjsupp/suad085.
  • Visseren FLJ, MacH F, Smulders YM, et al. 2021 ESC guidelines on cardiovascular disease prevention in clinical practiceDeveloped by the task force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Peventive Cardiology (EAPC). Eur Heart J. 2021 [cited 2023 May 28]. 42:3227–3337.
  • Raghunath S, Pfeifer JM, Ulloa-Cerna AE, et al. Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation–related stroke. Circulation. 2021 143(13):1287–1298. [cited 2023 May 28]. doi:10.1161/CIRCULATIONAHA.120.047829.
  • Cai A, Zhu Y, Clarkson SA, et al. The use of machine learning for the care of hypertension and heart failure. JACC Asia. 2021 [cited 2023 May 28]. 1:162.
  • Regitz-Zagrosek V, Roos-Hesselink JW, Bauersachs J, et al. 2018 ESC guidelines for the management of cardiovascular diseases during pregnancyThe task force for the management of cardiovascular diseases during pregnancy of the European Society of Cardiology (ESC). Eur Heart J. 2018 [cited 2023 May 28]. 39:3165–3241.
  • Schmidt LJ, Rieger O, Neznansky M, et al. A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes. Am J Obstet Gynecol. 2022 [cited 2023 May 28]. 227:.e77.1–.e77.30.
  • Chen J, Ji Y, Su T, et al. Prediction of adverse outcomes in De novo hypertensive disorders of pregnancy: development and validation of maternal and neonatal prognostic models. Healthcare (Basel). 2022 10(11):2307. [cited 2023 May 28]. doi:10.3390/healthcare10112307.
  • Von Dadelszen P, Payne B, Li J, et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet. 2011 377(9761):219–227. [cited 2023 May 28]. doi:10.1016/S0140-6736(10)61351-7.
  • Gupta K, Balyan K, Lamba B, et al. Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy. J Matern Fetal Neonatal Med. 2022 35(25):5587–5594. [cited 2023 May 28]. doi:10.1080/14767058.2021.1887847.
  • 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. [cited 2023 May 28]. doi:10.1016/j.ajogmf.2020.100250.
  • Van Der Tuuk K, Van Pampus MG, Koopmans CM, et al. Prediction of cesarean section risk in women with gestational hypertension or mild preeclampsia at term. European Journal Of Obstetrics & Gynecology And Reproductive Biologyquery. 2015 [cited 2023 May 28]. 191:23–27.
  • Koopmans CM, Bijlenga D, Groen H, et al. Induction of labour versus expectant monitoring for gestational hypertension or mild pre-eclampsia after 36 weeks’ gestation (HYPITAT): a multicentre, open-label randomised controlled trial. Lancet. 2009 374(9694):979–988. [cited 2023 May 28]. doi:10.1016/S0140-6736(09)60736-4.

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