Harvard and Beth Israel Deaconess Researchers Use Machine Learning Software Plus Human Intelligence to Improve Accuracy and Speed of Cancer Diagnoses

Machine learning software may help pathologists make earlier and more accurate diagnoses In Boston, two major academic centers are teaming up to apply big data and machine learning to the problem of diagnosing cancers earlier and with more accuracy. It is research that might have major implications for the anatomic pathology profession. A collaborative effort between teams at Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has resulted in an innovation that could...

New Solutions for Unstructured Data May Help With Clinical Laboratory and Anatomic Pathology Data

Existing unstructured anatomic pathology reports would directly benefit from novel word disambiguation approach under development at MIT Unstructured medical laboratory data is widely recognized to be one significant hurdle on the path toward the universal electronic health record (EHR). This is particularly true for anatomic pathology reports. Despite advances in synoptic reporting, to date, few pathology groups and clinical laboratories have developed ways to resolve this problem. Now there...
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