This is a new project called deCIPHER which is a collaboration between Microsoft researcher Lucy Vanderwende and University of Washington researchers which uses Microsoft Research toolkit. The project uses natural language processing (NLP) and machine learning to diagnose critical illnesses automatically from a patient’s electronic medical records.
They took the data of the electronic medical records in the diagnosis of pneumonia in approximately 100 patients being treated in the ICU at Seattle’s Harborview Medical Center. And they applied the NLP tools from Microsoft to identify the critical clinical information and trained the software using machine learning techniques. MSR claims that the software achieved a correct diagnosis with correct time-of-onset for positive cases in 84 percent of the patients.
Electronic medical records can be especially useful in the diagnosis of pneumonia, which has a nasty habit of appearing after a patient has been hospitalized in the intensive care unit (ICU). Currently, such diagnoses are made by consensus, after a thorough chart review of the patient’s medical tests and clinical notes.
This means that a physician must comb through hand-written records and copious test results to reach the correct diagnosis—a time-consuming, resource-intensive process. Now, my colleagues and I at the University of Washington, in collaboration with Microsoft researcher Lucy Vanderwende and using Microsoft Research Splat (Statistical Parsing and Linguistic Analysis Toolkit), have created deCIPHER, a project that demonstrates the potential to use natural language processing (NLP) and machine learning to diagnose such critical illnesses automatically from the patient’s electronic medical records.
Read more from the link below.