Using Audio Data to Facilitate Depression Risk Assessment in Primary
  Health Care
Adam Valen Levinson Abhay Goyal Roger Ho Chun Man Roy Ka-Wei Lee Koustuv Saha Nimay Parekh Frederick L. Altice Lam Yin Cheung Munmun De Choudhury Navin Kumar
Telehealth is a valuable tool for primary health care (PHC), where depression
is a common condition. PHC is the first point of contact for most people with
depression, but about 25% of diagnoses made by PHC physicians are inaccurate.
Many other barriers also hinder depression detection and treatment in PHC.
Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC and
improve overall diagnosis and treatment outcomes. Telehealth consultations
often have video issues, such as poor connectivity or dropped calls. Audio-only
telehealth is often more practical for lower-income patients who may lack
stable internet connections. Thus, our study focused on using audio data to
predict depression risk. The objectives were to: 1) Collect audio data from 24
people (12 with depression and 12 without mental health or major health
condition diagnoses); 2) Build a machine learning model to predict depression
risk. TPOT, an autoML tool, was used to select the best machine learning
algorithm, which was the K-nearest neighbors classifier. The selected model had
high performance in classifying depression risk (Precision: 0.98, Recall: 0.93,
F1-Score: 0.96). These findings may lead to a range of tools to help screen for
and treat depression. By developing tools to detect depression risk, patients
can be routed to AI-driven chatbots for initial screenings. Partnerships with a
range of stakeholders are crucial to implementing these solutions. Moreover,
ethical considerations, especially around data privacy and potential biases in
AI models, need to be at the forefront of any AI-driven intervention in mental
health care.