It might be easy to diagnose a cold based on a patient’s hoarse voice, but researchers believe subtle vocal changes undetectable to the human ear can identify or predict certain difficult-to-diagnose diseases.
That’s where machine learning could step in, helping physicians detect a range of illnesses that can't be diagnosed with more traditional tests, such as post-traumatic stress disorder, according to MIT Technology Review.
Charles Marmar a psychiatrist at New York University’s Langone Medical Center is collecting voice samples from combat veterans to determine how specific characteristics like tone and pitch could help psychiatrists diagnose PTSD, traumatic brain injury or depression. Initial studies have found vocal cues can differentiate PTSD patients from healthy ones with 77% accuracy, but an influx of new data will strengthen those results.
“Medical and psychiatric diagnosis will be more accurate when we have access to large amounts of biological and psychological data, including speech features,” Marmar told MIT Technology Review.
Data analytics is becoming increasingly important in the mental health field, where illnesses can be difficult to diagnose, even for experienced clinicians.
Recently, the Army rolled out a Behavioral Health Data Portal to help diagnose PTSD or depression, adding to its data-driven approach to predict suicide risks. The ONC has also launched several pilots focused on collecting patient-reported outcome data. Industry experts have also said healthcare leaders need to expand data collection efforts to include behavioral and socio-economic factors.
The Mayo Clinic is also collecting voice data combined with remote monitoring to identify patients with coronary artery disease. Meanwhile, a company in Boston is focusing solely on developing voice-based technology that can identify postpartum depression or signs of dementia, according to the MIT publication.