Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach

[1] Tavazzi L(2013)Should we revise our approach to ‘optimal medical therapy’? The case of chronic heart failure Eur Heart 34 2792-2794

[2] Maggioni AP(2017)Do patients have worse outcomes in heart failure than in cancer? A primary care-based cohort study with 10-year follow-up in Scotland Eur J Heart Fail 19 1095-1104

[3] Borer JS(2016)The prevention of hospital readmissions in heart failure Prog Cardiovasc Dis 58 379-385

[4] Mamas MA(2014)The global health and economic burden of hospitalisations for heart failure: lessons learned from hospitalized heart failure registries J Am Coll Cardiol 63 1123-1133

[5] Sperrin M(2015)Trajectories of risk after hospitalisation for heart failure, acute myocardial infarction, or pneumonia: retrospective cohort study BMJ 350 h411-279

[6] Watson MC(2014)Mining high-dimensional administrative claims data to predict early hospital hospitalisations J Am Med Inform Assoc 21 272-430

[7] Coutts A(2019)Dynamic prediction of hospital admission with medical claim data BMC Med Inform Decis Mak 43 111-211

[8] Wilde K(2019)LSTM model for prediction of heart failure in big data J Med Syst 9 e023724-71

[9] Burton C(2019)Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China BMJ Open 64 424-1243

[10] Kadam UT(2010)Routine health insurance data for scientific research: potential and limitations of the Agis Health Database J Clin Epidemiol 19 203-435

[1] Tavazzi L(2013)Should we revise our approach to ‘optimal medical therapy’? The case of chronic heart failure Eur Heart 34 2792-2794

[2] Maggioni AP(2017)Do patients have worse outcomes in heart failure than in cancer? A primary care-based cohort study with 10-year follow-up in Scotland Eur J Heart Fail 19 1095-1104

[3] Borer JS(2016)The prevention of hospital readmissions in heart failure Prog Cardiovasc Dis 58 379-385

[4] Mamas MA(2014)The global health and economic burden of hospitalisations for heart failure: lessons learned from hospitalized heart failure registries J Am Coll Cardiol 63 1123-1133

[5] Sperrin M(2015)Trajectories of risk after hospitalisation for heart failure, acute myocardial infarction, or pneumonia: retrospective cohort study BMJ 350 h411-279

[6] Watson MC(2014)Mining high-dimensional administrative claims data to predict early hospital hospitalisations J Am Med Inform Assoc 21 272-430

[7] Coutts A(2019)Dynamic prediction of hospital admission with medical claim data BMC Med Inform Decis Mak 43 111-211

[8] Wilde K(2019)LSTM model for prediction of heart failure in big data J Med Syst 9 e023724-71

[9] Burton C(2019)Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China BMJ Open 64 424-1243

[10] Kadam UT(2010)Routine health insurance data for scientific research: potential and limitations of the Agis Health Database J Clin Epidemiol 19 203-435