Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care

Abhay Goyal, Roger Ho Chun Man, Roy Ka Wei Lee, Koustuv Saha, Frederick L. Altice, Christian Poellabauer, Orestis Papakyriakopoulos, Lam Yin Cheung, Munmun De Choudhury, Kanica Allagh, Navin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Voice-only telehealth is often more practical for lower-income patients who may lack stable internet connections. Thus, our study focused on using voice data to predict depression risk. The objectives were to: 1) Collect voice 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), compared to previous models. These findings may lead to a range of tools to help screen for and treat depression.

Original languageEnglish
Title of host publicationCompanion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society
EditorsRaphael Heiberger, Ujwal Gadiraju, Marc Spaniol, Katharina Kinder-Kurlanda, Agnieszka Falenska, Afra Mashhadi, Jun Sun, Sierra Kaiser, Steffen Staab
PublisherAssociation for Computing Machinery, Inc
Pages17-18
Number of pages2
ISBN (Electronic)9798400704536
DOIs
StatePublished - 21 May 2024
Event16th ACM Web Science Conference, Websci Companion 2024 - Stuttgart, Germany
Duration: 21 May 202424 May 2024

Publication series

NameCompanion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society

Conference

Conference16th ACM Web Science Conference, Websci Companion 2024
Country/TerritoryGermany
CityStuttgart
Period21/05/2424/05/24

Keywords

  • Classification
  • Depression
  • Primary Care
  • Voice data

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