Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification

Making deep neural networks robust to label noise: A loss correction approach

Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017 · 1952

Earlier work this paper cites.

Icd-10-cm field testing project: Report on findings: Perceptions, ideas and recommendations from coding professionals across the nation

American Hospital Association et al. 2003 · 2003

Earlier work this paper cites.

Reliability of diagnoses coding with icd-10

Jürgen Stausberg, Nils Lehmann, Dirk Kaczmarek, and Markus Stein. 2008 · 2008

Earlier work this paper cites.

Multi-label learning with incomplete class assignments

Serhat Selcuk Bucak, Rong Jin, and Anil K Jain. 2011 · 2011

Earlier work this paper cites.

Learning with noisy labels

Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. 2013 · 2013

Earlier work this paper cites.

Deep classifiers from image tags in the wild

Hamid Izadinia, Bryan C Russell, Ali Farhadi, Matthew D Hoffman, and Aaron Hertzmann. 2015 · 2015

Earlier work this paper cites.

Classification with noisy labels by importance reweighting

Tongliang Liu and Dacheng Tao. 2015 · 2015

Earlier work this paper cites.

Learning from massive noisy labeled data for image classification

Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. 2015 · 2015

Earlier work this paper cites.

Semi-supervised multi-label learning with incomplete labels

Original

Feipeng Zhao and Yuhong Guo. 2015 · 2015

Cited alongside, same era.

Analyzing eight months of icd-10

Mary Butler. 2016 · 2016

Cited alongside, same era.

Mimic-iii, a freely accessible critical care database

Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016

Cited alongside, same era.

Multi-label ranking from positive and unlabeled data

Atsushi Kanehira and Tatsuya Harada. 2016 · 2016

Cited alongside, same era.

Accuracy and completeness of clinical coding using icd-10 for ambulatory visits

Jan Horsky, Elizabeth A Drucker, and Harley Z Ramelson. 2017 · 2017

Cited alongside, same era.

Scalable generative models for multi-label learning with missing labels

Learning from noisy large-scale datasets with minimal supervision

Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie. 2017 · 2017

Later among the works it cites.

Dimensionality-driven learning with noisy labels

Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. 2018 · 2018

Later among the works it cites.

Mlt-dfki at clef ehealth 2019: Multi-label classification of icd-10 codes with bert

Saadullah Amin, Günter Neumann, Katherine Dunfield, Anna Vechkaeva, Kathryn Annette Chapman, and Morgan Kelly Wixted. 2019 · 2019

Later among the works it cites.

Ontological attention ensembles for capturing semantic concepts in icd code prediction from clinical text

Matus Falis, Maciej Pajak, Aneta Lisowska, Patrick Schrempf, Lucas Deckers, Shadia Mikhael, Sotirios Tsaftaris, and Alison O’Neil. 2019 · 2019

Later among the works it cites.

Symmetric cross entropy for robust learning with noisy labels

Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. 2019 · 2019

Later among the works it cites.