Podcast: Improving Natural Language Processing Error Rates

[[{"type":"media","view_mode":"media_crop","fid":"11967","attributes":{"alt":"","class":"media-image media-image-right","id":"media_crop_7562901937715","media_crop_h":"0","media_crop_image_style":"-1","media_crop_instance":"207","media_crop_rotate":"0","media_crop_scale_h":"0","media_crop_scale_w":"0","media_crop_w":"0","media_crop_x":"0","media_crop_y":"0","style":"margin: 5px; float: right; border-width: 0px; border-style: solid;","title":" ","typeof":"foaf:Image"}}]]As many as 22 percent of radiology reports contain errors associated with natural language processing, said C. Matthew Hawkins, MD, a pediatric radiology fellow at Cincinnati Children’s Hospital Medical Center. Hawkins is speaking on the topic at the SIIM 2012 conference in Orlando this week.

In this podcast, Hawkins discusses how these errors can change the meaning of radiology reports and how augmenting standardized content associated with various types of speech recognition software can decrease mistakes.