by Angela Guess
ScienceDaily reports, “Machine learning has come of age in public health reporting according to researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis. They have found that existing algorithms and open source machine learning tools were as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. 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. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient’s treatment rather than at the time of initial diagnosis.”
Senior author Shaun Grannis, M.D., M.S., the interim director of the Regenstrief Center of Biomedical Informatics, commented, “We think that it’s no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not… We have come to the point in time that technology can handle this. A human’s time is better spent helping other humans by providing them with better clinical care… A lot of the work that we will be doing in informatics in the next few years will be focused on how we can benefit from machine learning and artificial intelligence. Everything — physician practices, health care systems, health information exchanges, insurers, as well as public health departments — are awash in oceans of data. How can we hope to make sense of this deluge of data? Humans can’t do it — but computers can.”
Photo credit: Flickr/ Todd Huffman