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References from Insights into heart failure metabolite markers through explainable machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Global public health burden of heart failure
10.15420/cfr.2016:25:2 · 2017 · External reference
10.1038/nrcardio.2016.25
10.1038/nrcardio.2016.25 · External reference
Heart failure with reduced ejection fraction
10.1038/nrdp.2017.58 · 2017 · External reference
Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events
10.1161/circgenetics.109.852814 · 2010 · External reference
Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease
10.1016/j.ahj.2012.02.005 · 2012 · External reference
10.1056/nejmoa1109400
10.1056/nejmoa1109400 · External reference
Prognostic implications of long-chain acylcarnitines in heart failure and reversibility with mechanical circulatory support
10.1016/j.jacc.2015.10.079 · 2016 · External reference
10.1161/jaha.115.003190
10.1161/jaha.115.003190 · External reference
10.1016/j.redox.2013.12.009
10.1016/j.redox.2013.12.009 · External reference
Circulating acylcarnitine profile in human heart failure: a surrogate of fatty acid metabolic dysregulation in mitochondria and beyond
10.1152/ajpheart.00820.2016 · 2017 · External reference
10.1088/0951-7715/21/8/t03
10.1088/0951-7715/21/8/t03 · External reference
Greedy function approximation: a gradient boosting machine
2001 · External reference
10.1111/rssb.12377
10.1111/rssb.12377 · External reference
All models are wrong, but many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously
2019 · External reference
Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation
10.1080/10618600.2014.907095 · 2015 · External reference
10.1145/2939672.2939778
10.1145/2939672.2939778 · 2016 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Regression tress with unbiased variable selection and interaction detection
2002 · External reference
10.1145/2487575.2487579
10.1145/2487575.2487579 · 2013 · External reference
10.1145/1390156.1390282
10.1145/1390156.1390282 · 2008 · External reference
Do little interactions get lost in dark random forests?
10.1186/s12859-016-0995-8 · 2016 · External reference
Interpreting tree ensembles with intrees
10.1007/s41060-018-0144-8 · 2019 · External reference
On the term ‘interaction’ and related phrases in the literature on random forests
10.1093/bib/bbu012 · 2015 · External reference
10.1214/07-aoas148
10.1214/07-aoas148 · External reference
Unresolved reference
2020 · External reference
Feature interaction in terms of prediction performance
10.3390/app9235191 · 2019 · External reference
Predictive case-based feature importance and interaction
10.1016/j.ins.2022.02.003 · 2022 · External reference
10.1093/nar/gkp356
10.1093/nar/gkp356 · External reference
Non-alcoholic fatty liver disease, and the underlying altered fatty acid metabolism, reveals brain hypoperfusion and contributes to the cognitive decline in app/ps1 mice
10.3390/metabo9050104 · 2019 · External reference
Unresolved reference
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Unresolved reference
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Unresolved reference
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Unresolved reference
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Unresolved reference
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Unresolved reference
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10.21105/joss.00786
10.21105/joss.00786 · External reference
10.1161/jaha.119.012673
10.1161/jaha.119.012673 · External reference
Blood glucose and heart failure in nondiabetic patients
10.2337/diacare.28.3.607 · 2005 · External reference
The relationship between cholesterol and survival in patients with chronic heart failure
10.1016/j.jacc.2003.07.016 · 2003 · External reference
Association of hdl cholesterol and triglycerides with mortality in patients with heart failure
10.1590/s0100-879x2009000500004 · 2009 · External reference
Long-chain acylcarnitines and monounsaturated fatty acids discriminate heart failure patients according to pulmonary hypertension status
10.3390/metabo11040196 · 2021 · External reference
The role of machine learning algorithms for diagnosing diseases
10.38094/jastt20179 · 2021 · External reference
Machine learning based decision support systems (dss) for heart disease diagnosis: a review
10.1007/s10462-017-9552-8 · 2018 · External reference
Metabolic disturbances identified in plasma are associated with outcomes in patients with heart failure: diagnostic and prognostic value of metabolomics
10.1016/j.jacc.2015.02.018 · 2015 · External reference
Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure
10.1038/s41598-019-56889-8 · 2020 · External reference
Circulating very long-chain saturated fatty acids and heart failure: the cardiovascular health study
10.1161/jaha.118.010019 · 2018 · External reference
Patterns of adipose tissue fatty acids and the risk of atrial fibrillation: a case-cohort study
10.1371/journal.pone.0208833 · 2018 · External reference
Circulating very-long-chain saturated fatty acids and incident coronary heart disease in us men and women
10.1161/circulationaha.114.014911 · 2015 · External reference
Association of circulating very long-chain saturated fatty acids with cardiovascular mortality in nhanes 2003-2004, 2011-2012
10.1210/clinem/dgad561 · 2024 · External reference
The continuous heart failure spectrum: moving beyond an ejection fraction classification
10.1093/eurheartj/ehz158 · 2019 · External reference
10.1038/nature14539
10.1038/nature14539 · External reference
