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

被引:9
作者
Alkhodari, Mohanad [1 ,2 ]
Xiong, Zhaohan [1 ]
Khandoker, Ahsan H. [2 ]
Hadjileontiadis, Leontios J. [2 ,3 ]
Leeson, Paul [1 ]
Lapidaire, Winok [1 ]
机构
[1] Univ Oxford, Radcliffe Dept Med, Cardiovasc Clin Res Facil, Oxford, England
[2] Khalifa Univ Sci & Tehcnol, Healthcare Engn Innovat Ctr HEIC, Dept Biomed Engn, Abu Dhabi, U Arab Emirates
[3] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, Thessaloniki, Greece
关键词
Hypertension disorders of pregnancy; preeclampsia; personalized medicine; artificial intelligence; machine learning; deep learning; CARDIOVASCULAR RISK STRATIFICATION; FUTURE; PREECLAMPSIA; CLASSIFICATION; PREDICTION; OUTCOMES; WOMEN;
D O I
10.1080/14779072.2023.2223978
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
IntroductionGuidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.Areas coveredIn this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.Expert opinionThe pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
引用
收藏
页码:531 / 543
页数:13
相关论文
共 124 条
[91]   Digital Twin in Industry: State-of-the-Art [J].
Tao, Fei ;
Zhan, He ;
Liu, Ang ;
Nee, A. Y. C. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (04) :2405-2415
[92]   Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression [J].
Tavazzi, Erica ;
Daberdaku, Sebastian ;
Zandona, Alessandro ;
Vasta, Rosario ;
Nefussy, Beatrice ;
Lunetta, Christian ;
Mora, Gabriele ;
Mandrioli, Jessica ;
Grisan, Enrico ;
Tarlarini, Claudia ;
Calvo, Andrea ;
Moglia, Cristina ;
Drory, Vivian ;
Gotkine, Marc ;
Chio, Adriano ;
Di Camillo, Barbara .
JOURNAL OF NEUROLOGY, 2022, 269 (07) :3858-3878
[93]   Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders [J].
Thong, Eleanor P. ;
Ghelani, Drishti P. ;
Manoleehakul, Pamada ;
Yesmin, Anika ;
Slater, Kaylee ;
Taylor, Rachael ;
Collins, Clare ;
Hutchesson, Melinda ;
Lim, Siew S. ;
Teede, Helena J. ;
Harrison, Cheryce L. ;
Moran, Lisa ;
Enticott, Joanne .
JOURNAL OF CARDIOVASCULAR DEVELOPMENT AND DISEASE, 2022, 9 (02)
[94]   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 [J].
Thorsen-Meyer, Hans-Christian ;
Nielsen, Annelaura B. ;
Nielsen, Anna P. ;
Kaas-Hansen, Benjamin Skov ;
Toft, Palle ;
Schierbeck, Jens ;
Strom, Thomas ;
Chmura, Piotr J. ;
Heimann, Marc ;
Dybdahl, Lars ;
Spangsege, Lasse ;
Hulsen, Patrick ;
Belling, Kirstine ;
Brunak, Soren ;
Perner, Anders .
LANCET DIGITAL HEALTH, 2020, 2 (04) :E179-E191
[95]   The classification, diagnosis and management of the hypertensive disorders of pregnancy: A revised statement from the ISSHP [J].
Tranquilli, A. L. ;
Dekker, G. ;
Magee, L. ;
Roberts, J. ;
Sibai, B. M. ;
Steyn, W. ;
Zeeman, G. G. ;
Brown, M. A. .
PREGNANCY HYPERTENSION-AN INTERNATIONAL JOURNAL OF WOMENS CARDIOVASCULAR HEALTH, 2014, 4 (02) :97-104
[96]   Precision medicine and the principle of equal treatment: a conjoint analysis [J].
Tranvag, Eirik Joakim ;
Strand, Roger ;
Ottersen, Trygve ;
Norheim, Ole Frithjof .
BMC MEDICAL ETHICS, 2021, 22 (01)
[97]   Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors [J].
Truslow, James G. ;
Goto, Shinichi ;
Homilius, Max ;
Mow, Christopher ;
Higgins, John M. ;
MacRae, Calum A. ;
Deo, Rahul C. .
CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES, 2022, 15 (06) :377-390
[98]  
Turing Alan M., 1950, Mind, V59, P433, DOI [10.1093/mind/LIX.236.433, DOI 10.1093/MIND/LIX.236.433]
[99]   Artificial intelligence for precision medicine in neurodevelopmental disorders [J].
Uddin, Mohammed ;
Wang, Yujiang ;
Woodbury-Smith, Marc .
NPJ DIGITAL MEDICINE, 2019, 2 (1)
[100]   Prediction of cesarean section risk in women with gestational hypertension or mild preeclampsia at term [J].
van der Tuuk, Karin ;
van Pampus, Maria G. ;
Koopmans, Corine M. ;
Aarnoudse, Jan G. ;
van den Berg, Paul P. ;
van Beek, Johannes J. ;
Copraij, Frans J. A. ;
Kleiverda, Gunilla ;
Porath, Martina ;
Rijnders, Robbert J. P. ;
van der Salm, Paulien C. M. ;
Morssink, Leonard P. ;
Stigter, Rob H. ;
Mol, Ben W. J. ;
Groen, Henk .
EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY, 2015, 191 :23-27