Machine ​learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.

ABSTRACT:

Aims

Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in-hospital mortality rates in HF cohorts on a population level based on administrative data comparing regression analysis with different machine learning (ML) models.

Methods and results

Inpatient cases with primary International Statistical Classification of Diseases and Related Health Problems (ICD-10) encoded discharge diagnosis of HF non-electively admitted to 86 German Helios hospitals between 1 January 2016 and 31 December 2018 were identified. The dataset was randomly split 75%/25% for model development and testing. Highly unbalanced variables were removed. Four ML

SUBMITTER: Konig S 

PROVIDER: S-EPMC8318394 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature