Krittanawong C, Bomback AS, Baber U, Bangalore S, Messerli FH, Wilson Tang WH Future course for utilizing synthetic intelligence to foretell and handle hypertension. Vol. 20, Present Hypertension Studies. Present Drugs Group LLC 1; 2018.
Sqalli MT, Al-Thani D. On how continual circumstances have an effect on the patient-ai interplay: a literature overview. Vol. 8, Healthcare (Switzerland). MDPI AG; 2020.
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Synthetic Intelligence Clinician learns optimum therapy methods for sepsis in intensive care. Nat Med. 2018;24:1716–20.
Google Scholar
Inoue Ok, Athey S, Tsugawa Y. Machine-learning-based high-benefit method versus typical high-risk method in blood stress administration. Int J Epidemiol. 2023;52:1243–56.
Google Scholar
Visco V, Izzo C, Mancusi C, Rispoli A, Tedeschi M, Virtuoso N, et al. Synthetic intelligence in hypertension administration: an ace up your sleeve. J Cardiovasc Dev Dis. 2023;10:74.
Singh J, Abrams KR, Bujkiewicz S. Incorporating single-arm research in meta-analysis of randomised managed trials: a simulation research. BMC Med Res Methodol. 2021;21:1–13.
Google Scholar
Persell SD, Peprah YA, Lipiszko D, Lee JY, Li JJ, Ciolino JD, et al. Impact of house blood stress monitoring through a smartphone hypertension teaching software or monitoring software on adults with uncontrolled hypertension: a randomized medical trial. JAMA Netw Open. 2020;3:e200255.
Google Scholar
Chiang PH, Wong M, Dey S. Utilizing wearables and machine studying to allow customized life-style suggestions to enhance blood stress. IEEE J Transl Eng Well being Med. 2021;9:2700513.
Google Scholar
Shamanna P, Dharmalingam M, Sahay R, Mohammed J, Mohamed M, Poon T, et al. Retrospective research of glycemic variability, BMI, and blood stress in diabetes sufferers within the Digital Twin Precision Remedy Program. Sci Rep. 2021;11:14892.
Google Scholar
Guthrie NL, Berman MA, Edwards KL, Appelbaum KJ, Dey S, Carpenter J, et al. Attaining speedy blood stress management with digital therapeutics: retrospective cohort and machine studying research. JMIR Cardio. 2019;3:e13030.
Google Scholar
Department OLH, Rikhy M, Auster-Gussman LA, Lockwood KG, Graham SA. Relationships between blood stress discount, weight reduction, and engagement in a digital app–primarily based hypertension care program: observational research. JMIR Kind Res. 2022;6:e38215.
Google Scholar
Satoh M, Maeda T, Hoshide S, Ohkubo T. Is antihypertensive therapy primarily based on house blood stress really useful relatively than that primarily based on workplace blood stress in adults with important hypertension? (meta-analysis). Hypertens Res. 2019;42:807–16.
Google Scholar
McManus RJ, Mant J, Franssen M, Nickless A, Schwartz C, Hodgkinson J, et al. Efficacy of self-monitored blood stress, with or with out telemonitoring, for titration of antihypertensive treatment (TASMINH4): an unmasked randomised managed trial. Lancet. 2018;391:949–59.
Google Scholar
Tucker KL, Sheppard JP, Stevens R, Bosworth HB, Bove A, Bray EP, et al. Self-monitoring of blood stress in hypertension: a scientific overview and particular person affected person knowledge meta-analysis. PLoS Med. 2017;14:e1002389.
Google Scholar
Fletcher BR, Hartmann-Boyce J, Hinton L, Mcmanus RJ. The impact of self-monitoring of blood stress on treatment adherence and life-style elements: a scientific overview and meta-analysis. Am J Hypertens. 2015;28:1209–21.
Google Scholar
