Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data

Details

• Alternative Title:
  Proc Mach Learn Res
• Personal Author:
  Bergquist, Savannah L. ; Brooks, Gabriel A. ; Keating, Nancy L. ; Landrum, Mary Beth ; Rose, Sherri
• Description:
  Research in oncology quality of care and health outcomes has been limited by the difficulty of identifying cancer stage in health care claims data. Using linked cancer registry and Medicare claims data, we develop a tool for classifying lung cancer patients receiving chemotherapy into early vs. late stage cancer by (|) deploying ensemble machine learning for prediction, (|) establishing a set of classification rules for the predicted probabilities, and (|) considering an augmented set of administrative claims data. We find our ensemble machine learning algorithm with a classification rule defined by the median substantially outperforms an existing clinical decision tree for this problem, yielding full sample performance of 93% sensitivity, 92% specificity, and 93% accuracy. This work has the potential for broad applicability as provider organizations, payers, and policy makers seek to measure quality and outcomes of cancer care and improve on risk adjustment methods.
• Subjects:
  Article
• Source:
  Proc Mach Learn Res. 68:25-38
• Pubmed ID:
  30542673
• Pubmed Central ID:
  PMC6287925
• Document Type:
  Journal Article
• Funding:
  HHSN261201000140C/CA/NCI NIH HHS/United States ; HHSN261201000035C/CA/NCI NIH HHS/United States ; T32 MH019733/MH/NIMH NIH HHS/United States ; HHSN261201000035I/CA/NCI NIH HHS/United States ; HHSN261201000034C/CA/NCI NIH HHS/United States ; U58 DP003862/DP/NCCDPHP CDC HHS/United States
• Volume:
  68
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:0a6052bb55df190c468feec0de63250542e69682859282e8b6b37d1d0851c0ca
• Download URL:
  https://stacks.cdc.gov/view/cdc/61402/cdc_61402_DS1.pdf
• File Type:
  [PDF - 971.40 KB ]