Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels

11/24/2018
by   Sabri Boughorbel, et al.
0

In this paper we are interested in the prediction of preterm birth based on diagnosis codes from longitudinal EHR. We formulate the prediction problem as a supervised classification with noisy labels. Our base classifier is a Recurrent Neural Network with an attention mechanism. We assume the availability of a data subset with both noisy and clean labels. For the cohort definition, most of the diagnosis codes on mothers' records related to pregnancy are ambiguous for the definition of full-term and preterm classes. On the other hand, diagnosis codes on babies' records provide fine-grained information on prematurity. Due to data de-identification, the links between mothers and babies are not available. We developed a heuristic based on admission and discharge times to match babies to their mothers and hence enrich mothers' records with additional information on delivery status. The obtained additional dataset from the matching heuristic has noisy labels and was used to leverage the training of the deep learning model. We propose an Alternating Loss Correction (ALC) method to train deep models with both clean and noisy labels. First, the label corruption matrix is estimated using the data subset with both noisy and clean labels. Then it is used in the model as a dense output layer to correct for the label noise. The network is alternately trained on epochs with the clean dataset with a simple cross-entropy loss and on next epoch with the noisy dataset and a loss corrected with the estimated corruption matrix. The experiments for the prediction of preterm birth at 90 days before delivery showed an improvement in performance compared with baseline and state of-the-art methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2020

Error-Bounded Correction of Noisy Labels

To collect large scale annotated data, it is inevitable to introduce lab...
research
12/01/2022

Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning

As the size of the dataset used in deep learning tasks increases, the no...
research
07/11/2023

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

In recent years, research on learning with noisy labels has focused on d...
research
08/04/2021

Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators

Multi-label learning is an emerging extension of the multi-class classif...
research
06/01/2021

Instance Correction for Learning with Open-set Noisy Labels

The problem of open-set noisy labels denotes that part of training data ...
research
11/06/2019

Addressing Ambiguity of Emotion Labels Through Meta-learning

Emotion labels in emotion recognition corpora are highly noisy and ambig...
research
08/06/2020

Salvage Reusable Samples from Noisy Data for Robust Learning

Due to the existence of label noise in web images and the high memorizat...

Please sign up or login with your details

Forgot password? Click here to reset