IMAGING ANALYTICS AND PATHOLOGY Improving imaging analytics and pathology with machine learning is of particular interest to healthcare organizations, who would otherwise be leaving a great deal of big data on the table..Machine learning can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses..A number of technology industry stalwarts have already started to invest heavily in imaging analytics and pathology projects..IBM Watson is rolling out a clinical imaging review service to help identify aortic stenosis, while Microsoft is targeting imaging biomarker phenotyping to supplement its cancer research efforts..Academic institutions are also getting in on the ground floor of advanced pattern recognition.. At Indiana University-Purdue University Indianapolis, researchers are turning machine learning algorithms loose on pathology slides to predict relapse rates for acute myelogenous leukemia. In a small study published earlier this year, one algorithm was able to identify patients who would relapse with 100 percent accuracy..And at Stanford University, machine learning tools performed better than human pathologists when distinguishing between two types of lung cancer.. The computer also bested its human counterparts at predicting patient survival times..Meanwhile, Google researchers have already exceeded the accuracy of human pathologists examining images of metastasized breast cancer tissue, reducing false negatives to one-quarter of the human clinical rate..NATURAL LANGUAGE PROCESSING AND FREE-TEXT DATA From the EHR to the MRI, unstructured data is everywhere in the healthcare industry – either intentionally or otherwise..PDF images of faxed lab reports, voice recordings of consumer interactions, and free-text EHR inputs all pose significant challenges for traditional analytics tools, but machine learning offers a novel way to extract usable meaning from these data sources..Read the full article Share this:Click to share on Twitter (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Google+ (Opens in new window)MoreClick to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window). More details