From population- to patient-based prediction of in-hospital mortality in heart failure using machine learning.

ABSTRACT:

Aims

Utilizing administrative data may facilitate risk prediction in heart failure inpatients. In this short report, we present different machine learning models that predict in-hospital mortality on an individual basis utilizing this widely available data source.

Methods and results

Inpatient cases with a main discharge diagnosis of heart failure hospitalized between 1 January 2016 and 31 December 2018 in one of 86 German Helios hospitals were examined. Comorbidities were defined by ICD-10 codes from administrative data. The data set was randomly split into 75/25% portions for model development and testing. Five algorithms were evaluated: logistic regression [generalized linear models (GLMs)], random forest (RF), gradient boosting machine (GBM), single-layer neural network

SUBMITTER: Konig S 

PROVIDER: S-EPMC9708014 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature