Deep learning uncertainty quantification for clinical text classification.

ABSTRACT:

Introduction

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated.

Method

In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Re

SUBMITTER: Peluso A 

PROVIDER: S-EPMC11467893 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature