Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Artificial Intelligence and Hypertension Management

  • Reference work entry
  • First Online:
Artificial Intelligence in Medicine

  • 355 Accesses

Abstract

The number of hypertensive patients is increasing worldwide. Since high blood pressure is strongly associated with the development of cardiovascular diseases, blood pressure control is essential. More than 90% of hypertensive patients have essential hypertension, which is caused by multiple factors, including lifestyle, physical constitution, and genetics. Blood pressure variability, which is the change in blood pressure over a certain period, is also associated with cardiovascular diseases. Therefore, regular blood pressure measurements outside the hospital for blood pressure control are required.

The increase in popularity of wearable devices and smartphones has made it easier than ever to gather biometric and environmental information. Blood pressure and lifestyle monitoring using these devices will improve our understanding of the timing of blood pressure rise and fall along with the factors contributing to blood pressure changes. Moreover, the development of novel analysis methods that provide alternatives to conventional statistical methods is expected to improve the accuracy of treatment effect estimations, taking into account individual interpatient differences.

This chapter describes the role of artificial intelligence for blood pressure measurement, factor analysis of blood pressure change, and blood pressure forecasting in personalized medicine for hypertension management. We summarize the current challenges and future outlooks for using artificial intelligence technologies for hypertension management.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhou B, Bentham J, Di Cesare M, Bixby H, Danaei G, Cowan MJ, et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet. 2017;389(10064):37–55. https://doi.org/10.1016/S0140-6736(16)31919-5.

    Article  Google Scholar 

  2. Carretero OA, Oparil S. Essential hypertension. Part I: Definition and etiology. Vol. 101, Circulation. Lippincott Williams and Wilkins; 2000. p. 329–35. https://doi.org/10.1161/01.CIR.101.3.329.

    Book  Google Scholar 

  3. Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71(6):E13–115. https://doi.org/10.1161/HYP.0000000000000065.

    Article  CAS  PubMed  Google Scholar 

  4. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021–104. https://doi.org/10.1093/eurheartj/ehy339.

    Article  PubMed  Google Scholar 

  5. Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese Society of hypertension guidelines for the management of hypertension (JSH 2019). Hypertens Res. 2019;42(9):1235–481. https://doi.org/10.1038/s41440-019-0284-9.

    Article  PubMed  Google Scholar 

  6. Chobanian AV. The hypertension paradox – more uncontrolled disease despite improved therapy. N Engl J Med. 2009;361(9):878–87. https://doi.org/10.1056/NEJMsa0903829.

    Article  CAS  PubMed  Google Scholar 

  7. Satoh A, Arima H, Ohkubo T, Nishi N, Okuda N, Ae R, et al. Associations of socioeconomic status with prevalence, awareness, treatment, and control of hypertension in a general Japanese population: NIPPON DATA 2010. J Hypertens. 2017;35(2):401–8.

    Article  CAS  PubMed  Google Scholar 

  8. Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354:i4098. https://doi.org/10.1136/bmj.i4098.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Basu S, Sussman JB, Hayward RA. Detecting heterogeneous treatment effects to guide personalized blood pressure treatment. Ann Intern Med. 2017;166(5):354. https://doi.org/10.7326/M16-1756.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mukkamala R, Hahn J-O, Inan OT, Mestha LK, Kim C-S, Toreyin H, et al. Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans Biomed Eng. 2015;62(8):1879–901. https://doi.org/10.1109/TBME.2015.2441951.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Su P, Ding X-R, Zhang Y-T, Liu J, Miao F, Zhao N. Long-term blood pressure prediction with deep recurrent neural networks. In: 2018 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE; 2018. p. 323–8. https://doi.org/10.1109/BHI.2018.8333434.

    Chapter  Google Scholar 

  12. Franco G, Cerina L, Gallicchio C, Micheli A, Santambrogio MD. Continuous blood pressure estimation through optimized echo state networks. In: International conference on artificial neural networks. Springer International Publishing; 2019. p. 48–61. https://doi.org/10.1007/978-3-030-30493-5_5.

    Chapter  Google Scholar 

  13. Eom H, Lee D, Han S, Hariyani YS, Lim Y, Sohn I, et al. End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism. Sensors. 2020;20(8):2338. https://doi.org/10.3390/s20082338.

    Article  PubMed Central  Google Scholar 

  14. Chandrasekhar A, Kim C-S, Naji M, Natarajan K, Hahn J-O, Mukkamala R. Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method. Sci Transl Med. 2018;10(431):eaap8674. https://doi.org/10.1126/scitranslmed.aap8674.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Schoettker P, Degott J, Hofmann G, Proença M, Bonnier G, Lemkaddem A, et al. Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements. Sci Rep. 2020;10(1):17827. https://doi.org/10.1038/s41598-020-74955-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ, et al. Smartphone-based blood pressure measurement using transdermal optical imaging technology. Circ Cardiovasc Imaging. 2019;12(8):8857. https://doi.org/10.1161/CIRCIMAGING.119.008857.

    Article  Google Scholar 

  17. Stergiou GS, Alpert BS, Mieke S, Wang J, O’Brien E. Validation protocols for blood pressure measuring devices in the 21st century. J Clin Hypertens. 2018;20(7):1096–9. https://doi.org/10.1111/jch.13294.

    Article  Google Scholar 

  18. Dörr M, Weber S, Birkemeyer R, Leonardi L, Winterhalder C, Raichle CJ, et al. iPhone app compared with standard blood pressure measurement –the iPARR trial. Am Heart J. 2021;233:102–8. https://doi.org/10.1016/j.ahj.2020.12.003.

    Article  PubMed  Google Scholar 

  19. SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373(22):2103–16. https://doi.org/10.1056/NEJMoa1511939.

    Article  CAS  Google Scholar 

  20. Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2015;113(523):1228–42. https://doi.org/10.1080/01621459.2017.1319839.

    Article  CAS  Google Scholar 

  21. Powers S, Qian J, Jung K, Schuler A, Shah NH, Hastie T, et al. Some methods for heterogeneous treatment effect estimation in high dimensions. Stat Med. 2018;37(11):1767–87. https://doi.org/10.1002/sim.7623.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Scarpa J, Bruzelius E, Doupe P, Le M, Faghmous J, Baum A. Assessment of risk of harm associated with intensive blood pressure management among patients with hypertension who smoke. JAMA Netw Open. 2019;2(3):e190005. https://doi.org/10.1001/jamanetworkopen.2019.0005.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci. 2019;116(10):4156–65. https://doi.org/10.1073/pnas.1804597116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Duan T, Rajpurkar P, Laird D, Ng AY, Basu S. Clinical value of predicting individual treatment effects for intensive blood pressure therapy. Circ Cardiovasc Qual Outcomes. 2019;12(3):e005010. https://doi.org/10.1161/CIRCOUTCOMES.118.005010.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Elshawi R, Al-Mallah MH, Sakr S. On the interpretability of machine learning-based model for predicting hypertension. BMC Med Inform Decis Mak. 2019;19(1):146. https://doi.org/10.1186/s12911-019-0874-0.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2016. p. 1135–44. https://doi.org/10.1145/2939672.2939778.

    Chapter  Google Scholar 

  27. Lundberg S, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;2017:4766–75.

    Google Scholar 

  28. Guthrie NL, Berman MA, Edwards KL, Appelbaum KJ, Dey S, Carpenter J, et al. Achieving rapid blood pressure control with digital therapeutics: retrospective cohort and machine learning study. JMIR Cardio. 2019;3(1):e13030. https://doi.org/10.2196/13030.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Beaulieu-Jones BK, Wu ZS, Williams C, Lee R, Bhavnani SP, Byrd JB, et al. Privacy-preserving generative deep neural networks support clinical data sharing. Circ Cardiovasc Qual Outcomes. 2019;12(7):5122. https://doi.org/10.1161/CIRCOUTCOMES.118.005122.

    Article  Google Scholar 

  30. Monahan M, Jowett S, Lovibond K, Gill P, Godwin M, Greenfield S, et al. Predicting out-of-office blood pressure in the clinic for the diagnosis of hypertension in primary care. Hypertension. 2018;71(2):250–61. https://doi.org/10.1161/HYPERTENSIONAHA.117.10244.

    Article  CAS  PubMed  Google Scholar 

  31. Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, et al. Genetic predisposition to high blood pressure and lifestyle factors: associations with midlife blood pressure levels and cardiovascular events. Circulation. 2018;137(7):653–61. https://doi.org/10.1161/CIRCULATIONAHA.117.030898.

    Article  PubMed  Google Scholar 

  32. Pei Z, Liu J, Liu M, Zhou W, Yan P, Wen S, et al. Risk-predicting model for incident of essential hypertension based on environmental and genetic factors with support vector machine. Interdiscip Sci Comput Life Sci. 2018;10(1):126–30. https://doi.org/10.1007/s12539-017-0271-2.

    Article  Google Scholar 

  33. Yang J, Liu F, Wang B, Chen C, Church T, Dukes L, et al. Blood pressure states transition inference based on multi-state Markov model. IEEE J Biomed Heal Informatics. 2020;25(1):237–46. https://doi.org/10.1109/JBHI.2020.3006217.

    Article  Google Scholar 

  34. Ye C, Fu T, Hao S, Zhang Y, Wang O, Jin B, et al. Prediction of incident hypertension within the next year: prospective study using statewide electronic health records and machine learning. J Med Internet Res. 2018;20(1):e22. https://doi.org/10.2196/jmir.9268.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2016. p. 785–94. https://doi.org/10.1145/2939672.2939785.

    Chapter  Google Scholar 

  36. Campos LF, Glickman ME, Hunter KB. Measuring effects of medication adherence on time-varying health outcomes using Bayesian dynamic linear models. Biostatistics. 2019;1–22. https://doi.org/10.1093/biostatistics/kxz059.

  37. Lacson RC, Baker B, Suresh H, Andriole K, Szolovits P, Lacson E. Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients. Clin Kidney J. 2019;12(2):206–12. https://doi.org/10.1093/ckj/sfy049.

    Article  PubMed  Google Scholar 

  38. Li X, Wu S, Wang L. Blood pressure prediction via recurrent models with contextual layer. In: Proceedings of the 26th international conference on world wide web. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee; 2017. p. 685–93. https://doi.org/10.1145/3038912.3052604.

    Chapter  Google Scholar 

  39. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Hiroshi Koshimizu & Yasushi Okuno

  2. Development Center, Omron Healthcare Co., Ltd., Kyoto, Japan

    Hiroshi Koshimizu

Authors
  1. Hiroshi Koshimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasushi Okuno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasushi Okuno .

Editor information

Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Koshimizu, H., Okuno, Y. (2022). Artificial Intelligence and Hypertension Management. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_263

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64573-1_263

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64572-4

  • Online ISBN: 978-3-030-64573-1

  • eBook Packages: MedicineReference Module Medicine

Publish with us

Policies and ethics

Search

Navigation