Researchers at the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis have found that existing algorithms and open source machine learning tools are as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. DATAVERSITY brought this news to us in their article, “Machine Learning is as Good or Better Than Humans at Detecting Cancer.” The computerized approach was also faster and less resource intensive in comparison to human counterparts.
Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. This results in busy health care providers submitting cancer reports to equally busy public health departments often months into the course of a patient’s treatment rather than at the time of initial diagnosis.
According to Shaun Grannis, M.D., M.S., the interim director of the Regenstrief Center of Biomedical Informatics, “This is not an advance in ideas, it’s a major infrastructure advance — we have the technology, we have the data, we have the software from which we saw accurate, rapid review of vast amounts of data without human oversight or supervision…A human’s time is better spent helping other humans by providing them with better clinical care.”
Melody K. Smith
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