Machine learning and deep learning tools for the automated capture of cancer surveillance data

Elizabeth Hsu, Heidi Hanson, Linda Coyle, Jennifer Stevens, Georgia Tourassi, Lynne Penberthy

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment.

Original languageEnglish
Pages (from-to)145-151
Number of pages7
JournalJournal of the National Cancer Institute - Monographs
Volume2024
Issue number65
DOIs
StatePublished - Aug 1 2024

Fingerprint

Dive into the research topics of 'Machine learning and deep learning tools for the automated capture of cancer surveillance data'. Together they form a unique fingerprint.

Cite this