Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers.

Journal: Artificial intelligence in medicine

PMID: 29754799

Abstract

OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality of certificates written in natural language. This paper proposes an automatic classification system for identifying all cancer related causes of death from death certificates.

Authors

Bevan Koopman

Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia; Queensland University of Technology, Brisbane, QLD, Australia.
Guido Zuccon

Queensland University of Technology, Brisbane, QLD, Australia.
Anthony Nguyen

Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia.
Anton Bergheim

Cancer Institute NSW, Sydney, Australia. Electronic address: anton.bergheim@cancerinstitute.org.au.
Narelle Grayson

Cancer Institute NSW, Sydney, Australia. Electronic address: Narelle.Grayson@cancerinstitute.org.au.

Keywords

Cause of Death Data Accuracy Data Mining Databases, Factual Death Certificates Humans Natural Language Processing Neoplasms New South Wales Rare Diseases Registries Reproducibility of Results Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (29754799)

Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers.

Abstract

Authors

Keywords

External Resources

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