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
[1]   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].
Ahmad, Tariq ;
Lund, Lars H. ;
Rao, Pooja ;
Ghosh, Rohit ;
Warier, Prashant ;
Vaccaro, Benjamin ;
Dahlstrom, Ulf ;
O'Connor, Christopher M. ;
Felker, G. Michael ;
Desai, Nihar R. .
JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2018, 7 (08)
[2]   Pre-Eclampsia and Future Cardiovascular Risk Among Women A Review [J].
Ahmed, Raheel ;
Dunford, Joseph ;
Mehran, Roxana ;
Robson, Stephen ;
Kunadian, Vijay .
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2014, 63 (18) :1815-1822
[3]   Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models [J].
Alkhodari, Mohanad ;
Jelinek, Herbert F. ;
Werghi, Naoufel ;
Hadjileontiadis, Leontios J. ;
Khandoker, Ahsan H. .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2021, 25 (03) :746-754
[4]   Analyzing Patient Trajectories With Artificial Intelligence [J].
Allam, Ahmed ;
Feuerriegel, Stefan ;
Rebhan, Michael ;
Krauthammer, Michael .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (12)
[5]   Cardiovascular Health Trajectories From Childhood Through Middle Age and Their Association With Subclinical Atherosclerosis [J].
Allen, Norrina B. ;
Krefman, Amy E. ;
Labarthe, Darwin ;
Greenland, Philip ;
Juonala, Markus ;
Kahonen, Mika ;
Lehtimaki, Terho ;
Day, R. Sue ;
Bazzano, Lydia A. ;
Van Horn, Linda V. ;
Liu, Lei ;
Alonso, Camilo Fernandez ;
Webber, Larry S. ;
Pahkala, Katja ;
Laitinen, Tomi T. ;
Raitakari, Olli T. ;
Lloyd-Jones, Donald M. .
JAMA CARDIOLOGY, 2020, 5 (05) :557-566
[6]   Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure [J].
Bai, Yongyi ;
Yao, Haishen ;
Jiang, Xuehan ;
Bian, Suyan ;
Zhou, Jinghui ;
Sun, Xingzhi ;
Hu, Gang ;
Sun, Lan ;
Xie, Guotong ;
He, Kunlun .
FRONTIERS IN PHARMACOLOGY, 2022, 12
[7]   A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications [J].
Barricelli, Barbara Rita ;
Casiraghi, Elena ;
Fogli, Daniela .
IEEE ACCESS, 2019, 7 :167653-167671
[8]   Deep Learning in Cardiology [J].
Bizopoulos, Paschalis ;
Koutsouris, Dimitrios .
IEEE REVIEWS IN BIOMEDICAL ENGINEERING, 2019, 12 :168-193
[9]  
Bostrom N., 2014, NICK BOSTROM SUPERIN
[10]   Digital Twins: From Personalised Medicine to Precision Public Health [J].
Boulos, Maged N. Kamel ;
Zhang, Peng .
JOURNAL OF PERSONALIZED MEDICINE, 2021, 11 (08)