Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing

Climie R, et al. Vascular ageing in youth: a call to action. Heart Lung Circ. 2021. https://doi.org/10.1016/j.hlc.2021.06.516.

Article  Google Scholar 

Laurent S, Katsahian S, Fassot C, Tropeano A, Gautier I, Laloux B, Boutouyrie P. Aortic stiffness is an independent predictor of fatal stroke in essential hypertension. Stroke. 2003;34:1203–6. https://doi.org/10.1161/01.STR.0000065428.03209.64.

Article  Google Scholar 

Kotsis V, Antza C, Doundoulakis I, Stabouli S. Markers of early vascular ageing. Curr Pharm Des. 2017;23(22):3200–4. https://doi.org/10.2174/1381612823666170328142433.

Article  Google Scholar 

Kılıç A, et al. Role of dyslipidemia in early vascular aging syndrome. Turkish J Med Sci. 2021;51(2):727–34. https://doi.org/10.3906/sag-2008-165.

Article  Google Scholar 

Nilsson P, et al. Early vascular aging in hypertension. Front Cardiovasc Med. 2020;7:6. https://doi.org/10.3389/fcvm.2020.00006.

Article  Google Scholar 

Bikia V, et al. Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research. Eur Heart J-Digital Health. 2021;2(4):676–90. https://doi.org/10.1093/ehjdh/ztab089.

Article  Google Scholar 

Hors-Fraile S, et al. Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: a scoping review. Int J Med Inform. 2017;114:143–55. https://doi.org/10.1016/j.ijmedinf.2017.12.018.

Article  Google Scholar 

Saz-Lara A, et al. Early vascular ageing as an index of cardiovascular risk in healthy adults: confirmatory factor analysis from the EVasCu study. Cardiovasc Diabetol. 2023;22(1):209. https://doi.org/10.1186/s12933-023-01947-9.

Article  Google Scholar 

Cavero-Redondo I, et al. Validation of an early vascular ageing construct model for comprehensive cardiovascular risk assessment using external risk indicators for improved clinical utility: data from the EVasCu study. Cardiovasc Diabetol. 2024;23(1):33. https://doi.org/10.1186/s12933-023-02104-y.

Article  Google Scholar 

Oliveira JS, et al. Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: a systematic review and meta-analysis. Br J Sports Med. 2020;54(20):1188–94. https://doi.org/10.1136/bjsports-2018-100324.

Article  Google Scholar 

Espín V, Hurtado MV, Noguera M. Nutrition for Elder Care: a nutritional semantic recommender system for the elderly. Expert Syst. 2016;33:201–10. https://doi.org/10.1111/exsy.12143.

Article  Google Scholar 

Giabbanelli PJ, Crutzen R. Supporting self-management of obesity using a novel game architecture. Health Inform J. 2015;21(3):223–36. https://doi.org/10.1177/1460458214521051.

Article  Google Scholar 

Hidalgo JI, et al. glUCModel: a monitoring and modeling system for chronic diseases applied to diabetes. J Biomed Inform. 2014;48:183–92. https://doi.org/10.1016/j.jbi.2013.12.015.

Article  Google Scholar 

Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 2017;18(1):18. https://doi.org/10.1186/s12859-016-1415-9.

Article  Google Scholar 

Potter G, et al. Putting the collaborator back into collaborative filtering. 2008. https://doi.org/10.1145/1722149.1722152.

Nilsson P, et al. The concept of early vascular ageing—an update in 2015. EMJ Diabetes. 2015. https://doi.org/10.33590/emjdiabet/10312465.

Article  Google Scholar 

Tavallali P, et al. Artificial intelligence estimation of carotid-femoral pulse wave velocity using carotid waveform. Sci Rep. 2018;8(1):1014. https://doi.org/10.1038/s41598-018-19457-0.

Article  Google Scholar 

Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res. 2017;121(9):1092–101. https://doi.org/10.1161/CIRCRESAHA.117.311312.

Article  Google Scholar 

Alaa AM, et al. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423604 UK Biobank participants. PLoS ONE. 2019;14(5): e0213653. https://doi.org/10.1371/journal.pone.0213653.

Article  Google Scholar 

Al’Aref SJ, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J. 2020;41(3):359–67. https://doi.org/10.1093/eurheartj/ehz565.

Article  Google Scholar 

Garcia-Carretero R, et al. Pulse wave velocity and machine learning to predict cardiovascular outcomes in prediabetic and diabetic populations. J Med Syst. 2019;44(1):16. https://doi.org/10.1007/s10916-019-1479-y.

Article  Google Scholar 

Jamthikar A, et al. A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovasc Diagn Ther. 2019;9(5):420–30. https://doi.org/10.21037/cdt.2019.09.03.

Article  Google Scholar 

Kakadiaris IA, et al. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7(22):e009476. https://doi.org/10.1161/JAHA.118.009476.

Article  Google Scholar 

Sorelli M, et al. Detecting vascular age using the analysis of peripheral pulse. IEEE Trans Biomed Eng. 2018;65(12):2742–50. https://doi.org/10.1109/TBME.2018.2814630.

Article  Google Scholar 

Vallée A, et al. Added value of aortic pulse wave velocity index in a predictive diagnosis decision tree of coronary heart disease. Am J Hypertens. 2019;32(4):375–83. https://doi.org/10.1093/ajh/hpz004.

Article  Google Scholar 

Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans R Soc A. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202.

Article  MathSciNet  Google Scholar 

Loohach Richa, et al. Effect of distance functions on simple K-means clustering algorithm. Int J Comput Appl. 2012;49:7–9. https://doi.org/10.5120/7629-0698.

Article  Google Scholar 

Nocedal J, et al. Numerical optimization. New York: Springer; 2006. https://doi.org/10.1007/b98874.

Book  Google Scholar 

Caliński Tadeusz, Harabasz Joachim. A dendrite method for cluster analysis. Commun Stat-Theory Methods. 1974;3:1–27.

Article  MathSciNet  Google Scholar 

Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell. 1979;1(2):224–7. https://doi.org/10.1109/TPAMI.1979.

Article  Google Scholar 

Rousseeuw P. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20(1):53–65. https://doi.org/10.1016/0377-0427(87)90125-7.

Article  Google Scholar 

Rahimi SA, Cwintal M, Huang Y, Ghadiri P, Grad R, Poenaru D, Gore G, Zomahoun H, Légaré F, Pluye P. Application of artificial intelligence in shared decision making: scoping review. JMIR Med Inform. 2022;10:36199. https://doi.org/10.2196/36199.

Article  Google Scholar 

Poon AIF, Sung JJY. Opening the black box of AI-medicine. J Gastroenterol Hepatol. 2021;36(3):581–4. https://doi.org/10.1111/jgh.15384.

Article  Google Scholar 

Garcia-Vidal C, et al. Artificial intelligence to support clinical decision-making processes. EBioMedicine. 2019;46:27–9. https://doi.org/10.1016/j.ebiom.2019.07.019.

Article  Google Scholar 

Eagleson R. y Sandrine de Ribaupierre. Human-machine interfaces for medical imaging and clinical interventions. 2020.

Haynes R, McKibbon K, Kanani R. Systematic review of randomised trials of interventions to assist patients to follow prescriptions for medications. The Lancet. 1996;348:383–6. https://doi.org/10.1016/S0140-6736(96)01073-2.

Article  Google Scholar 

Pereira MG, Pedras S, Ferreira G, Machado J. Differences, predictors, and moderators of therapeutic adherence in patients recently diagnosed with type 2 diabetes. J Health Psychol. 2018;25:1871–81. https://doi.org/10.1177/1359105318780505.

Article  Google Scholar 

Yuan K, Bentler P. Effect of outliers on estimators and tests in covariance structure analysis. Br J Math Stat Psychol. 2001;54(Pt 1):161–75. https://doi.org/10.1348/000711001159366.

Article  Google Scholar 

Kaya IE, Pehlivanli AC, Sekizkardes EG, Ibrikci T. PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Prog Biomed. 2017;140:19–28. https://doi.org/10.1016/j.cmpb.2016.11.011.

Article  Google Scholar 

Flores AM, et al. Unsupervised learning for automated detection of coronary artery disease subgroups. J Am Heart Assoc. 2021;10: e021976. https://doi.org/10.1161/JAHA.121.021976.

Article  Google Scholar 

McClelland RL, et al. Arterial age as a function of coronary artery calcium (from the multi-ethnic study of atherosclerosis [MESA]). Am J Cardiol. 2009;103(1):59–63. https://doi.org/10.1016/j.amjcard.2008.08.031.

Article  Google Scholar 

McClelland RL, et al. 10-year coronary heart disease risk prediction using coronary artery calcium and traditional risk factors: derivation in the MESA (multi-ethnic study of atherosclerosis) with validation in the HNR (Heinz Nixdorf Recall) study and the DHS (Dallas Heart Study). J Am Coll Cardiol. 2015;66(15):1643–53. https://doi.org/10.1016/j.jacc.2015.08.035.

Article  Google Scholar 

Bild DE, et al. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871–81. https://doi.org/10.1093/aje/kwf113.

Article  Google Scholar 

Cai Y, et al. Health recommender systems development, usage, and evaluation from 2010 to 2022: a scoping review. Int J Environ Res Public Health. 2022;19(22):15115. https://doi.org/10.3390/ijerph192215115.

Article  Google Scholar 

Ferretto LR, et al. A physical activity recommender system for patients with arterial hypertension. IEEE Access. 2020;8:61656–64. https://doi.org/10.1109/ACCESS.2020.2983564.

Article  Google Scholar 

Daskalova N, Lee B, Huang J, Ni C, Lundin J. Investigating the effectiveness of cohort-based sleep recommendations. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2018;2(3):1–19. https://doi.org/10.1145/3264911.

Article  Google Scholar 

Sridevi M, Rao RR. Finding right doctors and hospitals: a personalized health recommender. In: Fong S, Akashe S, Mahalle P, editors. Information and communication technology for competitive strategies. Lecture Notes in Networks and Systems, vol. 40. Singapore: Springer; 2019. https://doi.org/10.1007/978-981-13-0586-3_69.

Chapter  Google Scholar 

Mustaqeem A, Anwar SM, Majid M. A modular cluster based collaborative recommender sy

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