Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

ABSTRACT:

Background

Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation.

Objective

In this study, we leveraged machine learning techniques to build a FN mortality risk evaluation tool focused on FN admissions without physicians' subjective evaluation.

Methods

We used the National Inpatient Sample and Nationwide Inpatient Sample (NIS) that included mortality data among adult inpatients who were diagnosed with FN during a hospital admission. Machine learning techniques that we compared included linear models (ridge logistic regression and linear support vector machine) and non-linear models (gra

SUBMITTER: Du X 

PROVIDER: S-EPMC7255942 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature