2020
DOI: 10.1016/j.acvd.2020.03.012
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Heart failure with preserved ejection fraction: A clustering approach to a heterogenous syndrome

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Cited by 26 publications
(42 citation statements)
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“…The current analysis has clustered the largest HFpEF population to date. Our analysis was conducted in a real‐world registry population, compared other studies which were trials, 10 , 11 , 13 traditional cohorts 12 , 14 and a small electronic health record study. 9 In online supplementary Table S5 , we show the differences and similarities between clustering studies.…”
Section: Discussionmentioning
confidence: 99%
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“…The current analysis has clustered the largest HFpEF population to date. Our analysis was conducted in a real‐world registry population, compared other studies which were trials, 10 , 11 , 13 traditional cohorts 12 , 14 and a small electronic health record study. 9 In online supplementary Table S5 , we show the differences and similarities between clustering studies.…”
Section: Discussionmentioning
confidence: 99%
“…Overall, the clusters that we found were similar to those in previous studies. 9 , 10 , 11 , 12 , 13 , 14 We identified five main HFpEF phenotypes in all studies combined. These phenotypes correspond to the clusters we describe in our study.…”
Section: Discussionmentioning
confidence: 99%
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“…We have identified at least 14 reports of machine learning used to investigate patients with HFpEF. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] They show Figure 1 Known risk factors for heart failure with preserved ejection fraction (HFpEF) are listed in the box on the left, in their order in the European Society of Cardiology consensus recommendations. 11 The Venn diagram in the middle box shows which broad types of variables were used as input to the machine learning studies [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] that are summarised in Table 3.…”
Section: Phenotyping By Artificial Intelligencementioning
confidence: 99%