Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

Overview

Journal Clin Pract

Publisher MDPI

Specialty General Medicine

Date 2024 Jul 25

PMID 39051303

Authors

Sreyoshi F Alam

Maria L Gonzalez Suarez

Affiliations

Soon will be listed here.

Abstract

This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at an individual and global level. We focus on five key areas: risk prediction, diagnosis, education, monitoring, and management of hypertension, supplemented with a brief look into the works on hypertensive disease of pregnancy. For each area, we discuss the advantages and disadvantages of integrating AI. While AI, in its current rudimentary form, cannot replace sound clinical judgment, it can still enhance faster diagnosis, education, prevention, and management. The integration of AI in healthcare is poised to revolutionize hypertension care, although careful implementation and ongoing research are essential to mitigate risks.

References

Jhee J, Lee S, Park Y, Lee S, Kim Y, Kang S . Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One. 2019; 14(8):e0221202. PMC: 6707607. DOI: 10.1371/journal.pone.0221202. View

Lee S, Nam Y, Choi E, Jung Y, Sriram V, Leiby J . Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning. Sci Rep. 2022; 12(1):15793. PMC: 9499925. DOI: 10.1038/s41598-022-15391-4. View

Wu L, Huang L, Li M, Xiong Z, Liu D, Liu Y . Differential diagnosis of secondary hypertension based on deep learning. Artif Intell Med. 2023; 141:102554. DOI: 10.1016/j.artmed.2023.102554. View

Pilz N, Patzak A, Bothe T . Continuous cuffless and non-invasive measurement of arterial blood pressure-concepts and future perspectives. Blood Press. 2022; 31(1):254-269. DOI: 10.1080/08037051.2022.2128716. View

Villalain C, Herraiz I, Dominguez-Del Olmo P, Angulo P, Ayala J, Galindo A . Prediction of Delivery Within 7 Days After Diagnosis of Early Onset Preeclampsia Using Machine-Learning Models. Front Cardiovasc Med. 2022; 9:910701. PMC: 9283699. DOI: 10.3389/fcvm.2022.910701. View

Sarraju A, Bruemmer D, Van Iterson E, Cho L, Rodriguez F, Laffin L . Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model. JAMA. 2023; 329(10):842-844. PMC: 10015303. DOI: 10.1001/jama.2023.1044. View

DeMarzo A . Clinical Use of Impedance Cardiography for Hemodynamic Assessment of Early Cardiovascular Disease and Management of Hypertension. High Blood Press Cardiovasc Prev. 2020; 27(3):203-213. DOI: 10.1007/s40292-020-00383-0. View

Islam M, Rahman M, Roy D, Tawabunnahar M, Jahan R, Ahmed N . Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh. Diabetes Metab Syndr. 2021; 15(3):877-884. DOI: 10.1016/j.dsx.2021.03.035. View

Singhal K, Azizi S, Tu T, Mahdavi S, Wei J, Chung H . Large language models encode clinical knowledge. Nature. 2023; 620(7972):172-180. PMC: 10396962. DOI: 10.1038/s41586-023-06291-2. View

10.

Kumar N, Hirshberg A, Srinivas S . Best Practices for Managing Postpartum Hypertension. Curr Obstet Gynecol Rep. 2022; 11(3):159-168. PMC: 9207847. DOI: 10.1007/s13669-022-00343-6. View

11.

Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H . BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform. 2022; 23(6). DOI: 10.1093/bib/bbac409. View

12.

Wang G, Zhang Y, Li S, Zhang J, Jiang D, Li X . A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia. Front Cardiovasc Med. 2021; 8:736491. PMC: 8578855. DOI: 10.3389/fcvm.2021.736491. View

13.

Islam S, Talukder A, Awal M, Siddiqui M, Ahamad M, Ahammed B . Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries. Front Cardiovasc Med. 2022; 9:839379. PMC: 9008259. DOI: 10.3389/fcvm.2022.839379. View

14.

Attivissimo F, DAlessandro V, De Palma L, Lanzolla A, Di Nisio A . Non-Invasive Blood Pressure Sensing via Machine Learning. Sensors (Basel). 2023; 23(19). PMC: 10574845. DOI: 10.3390/s23198342. View

15.

OHagan E, McIntyre D, Laranjo L . The Potential for a Chat-Based Artificial Intelligence Model to Facilitate Educational Messaging on Hypertension. Hypertension. 2023; 80(8):e128-e130. DOI: 10.1161/HYPERTENSIONAHA.123.21395. View

16.

Zhang X, Meng Y, Jiang M, Yang L, Zhang K, Lian C . Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy. Math Biosci Eng. 2023; 20(5):8308-8319. DOI: 10.3934/mbe.2023363. View

17.

Liao B, Jia X, Zhang T, Sun R . DHDIP: An interpretable model for hypertension and hyperlipidemia prediction based on EMR data. Comput Methods Programs Biomed. 2022; 226:107088. DOI: 10.1016/j.cmpb.2022.107088. View

18.

Joung J, Jung C, Lee H, Chae M, Kim H, Park J . Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations. Sci Rep. 2023; 13(1):8605. PMC: 10224938. DOI: 10.1038/s41598-023-35492-y. View

19.

Nguyen T, Le H, Hwang K, Hong Y, Kim J . Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models. Biomedicines. 2022; 10(6). PMC: 9220060. DOI: 10.3390/biomedicines10061406. View

20.

Chu Y, Tang K, Hsu Y, Huang T, Wang D, Li W . Non-invasive arterial blood pressure measurement and SpO estimation using PPG signal: a deep learning framework. BMC Med Inform Decis Mak. 2023; 23(1):131. PMC: 10362790. DOI: 10.1186/s12911-023-02215-2. View