Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

Kevin De Angeli, Shang Gao, Ioana Danciu, Eric B. Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Mark Damesyn, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Hong Jun Yoon

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.

Original languageEnglish
Article number103957
JournalJournal of Biomedical Informatics
Volume125
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
The Utah Cancer Registry is funded by the National Cancer Institute’s SEER Program, Contract No. HHSN261201800016I,and the US Centers for Disease Control and Prevention’s National Program of Cancer Registries, Cooperative Agreement No. NU58DP0063200, with additional support from the University of Utah and Huntsman Cancer Foundation.

Funding Information:
This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by DOE and the NCI of the National Institutes of Health. This work was performed under the auspices of DOE by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, and Oak Ridge National Laboratory under Contract DE-AC05-00OR22725.

Funding Information:
NJSCR data were collected using funding from NCI and the Surveillance, Epidemiology and End Results (SEER) Program (HHSN261201300021I), the CDC’s National Program of Cancer Registries (NPCR) (NU58DP006279-02–00) as well as the State of New Jersey and the Rutgers Cancer Institute of New Jersey.

Funding Information:
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the DOE Office of Science under Contract No. DE-AC05-00OR22725.

Funding Information:
The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s (CDC) National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the author(s) and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors.

Funding Information:
LTR data were collected using funding from NCI and the Surveillance, Epidemiology and End Results (SEER) Program (HHSN261201800007I), the CDC’s National Program of Cancer Registries (NPCR) (NU58DP006332-02–00) as well as the State of Louisiana.

Funding Information:
The Cancer Surveillance System is supported by the National Cancer Institute’s SEER Program (Contract Award HHSN261291800004I) and with additional funds provided by the Fred Hutchinson Cancer Research Center.

Funding Information:
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US Department of Energy (DOE) Office of Science and the National Nuclear Security Administration.

Funding Information:
KCR data were collected with funding from NCI Surveillance, Epidemiology and End Results (SEER) Program (HHSN261201800013I), the CDC National Program of Cancer Registries (NPCR) (U58DP00003907) and the Commonwealth of Kentucky.

Funding Information:
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US Department of Energy (DOE) Office of Science and the National Nuclear Security Administration. This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by DOE and the NCI of the National Institutes of Health. This work was performed under the auspices of DOE by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, and Oak Ridge National Laboratory under Contract DE-AC05-00OR22725. The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention's (CDC) National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the National Cancer Institute's Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the author(s) and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. KCR data were collected with funding from NCI Surveillance, Epidemiology and End Results (SEER) Program (HHSN261201800013I), the CDC National Program of Cancer Registries (NPCR) (U58DP00003907) and the Commonwealth of Kentucky. LTR data were collected using funding from NCI and the Surveillance, Epidemiology and End Results (SEER) Program (HHSN261201800007I), the CDC's National Program of Cancer Registries (NPCR) (NU58DP006332-02?00) as well as the State of Louisiana. NJSCR data were collected using funding from NCI and the Surveillance, Epidemiology and End Results (SEER) Program (HHSN261201300021I), the CDC's National Program of Cancer Registries (NPCR) (NU58DP006279-02?00) as well as the State of New Jersey and the Rutgers Cancer Institute of New Jersey. New Mexico Tumor Registry's participation in this project was supported by Contract HHSN261201800014I, Task Order HHSN26100001 from the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) Program. The Cancer Surveillance System is supported by the National Cancer Institute's SEER Program (Contract Award HHSN261291800004I) and with additional funds provided by the Fred Hutchinson Cancer Research Center. The Utah Cancer Registry is funded by the National Cancer Institute's SEER Program, Contract No. HHSN261201800016I,and the US Centers for Disease Control and Prevention's National Program of Cancer Registries, Cooperative Agreement No. NU58DP0063200, with additional support from the University of Utah and Huntsman Cancer Foundation. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the DOE Office of Science under Contract No. DE-AC05-00OR22725.

Funding Information:
New Mexico Tumor Registry’s participation in this project was supported by Contract HHSN261201800014I, Task Order HHSN26100001 from the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) Program.

Publisher Copyright:
© 2021

Keywords

  • CNN
  • Class Imbalance
  • Deep Learning
  • Ensemble
  • NLP
  • Text Classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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