Regenstrief: Machines faster than humans at detecting cancer

Algorithms and open-source machine-learning tools are as good as, or even better than, human reviewers in detecting cancer cases using information from free-text pathology reports, according to a new study from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University at Purdue. Further, the computerized approach also was faster and less resource-intensive. Researchers sampled 7,000 free-text pathology reports from more than 30 hospitals that participate in the Indiana Health Information Exchange. The researchers used open-source tools, classification algorithms, and varying feature selection approaches to predict if a report was positive or negative for cancer. The results indicated that a fully automated review yielded results similar or better than those of trained human reviewers, saving both time and money, Indiana University said.