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

doi:10.1145/3630744.3658408

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

Assistant Professorship of Societal Computing

Missouri University of Science and Technology
NUS
Singapore University of Technology and Design
University of Illinois Urbana-Champaign
Yale University
Florida International University
Kindmodels
Georgia Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	English
Title of host publication	Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society
Editors	Raphael Heiberger, Ujwal Gadiraju, Marc Spaniol, Katharina Kinder-Kurlanda, Agnieszka Falenska, Afra Mashhadi, Jun Sun, Sierra Kaiser, Steffen Staab
Publisher	Association for Computing Machinery, Inc
Pages	17-18
Number of pages	2
ISBN (Electronic)	9798400704536
DOIs	https://doi.org/10.1145/3630744.3658408
State	Published - 13 Jun 2024
Event	16th ACM Web Science Conference, Websci Companion 2024 - Stuttgart, Germany Duration: 21 May 2024 → 24 May 2024

Publication series

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

Conference

Conference	16th ACM Web Science Conference, Websci Companion 2024
Country/Territory	Germany
City	Stuttgart
Period	21/05/24 → 24/05/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Classification
Depression
Primary Care
Voice data

Access to Document

10.1145/3630744.3658408

Cite this

Goyal, A., Ho Chun Man, R., Lee, R. K. W., Saha, K., L. Altice, F., Poellabauer, C., Papakyriakopoulos, O., Yin Cheung, L., De Choudhury, M., Allagh, K., & Kumar, N. (2024). Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. In R. Heiberger, U. Gadiraju, M. Spaniol, K. Kinder-Kurlanda, A. Falenska, A. Mashhadi, J. Sun, S. Kaiser, & S. Staab (Eds.), Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society (pp. 17-18). (Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society). Association for Computing Machinery, Inc. https://doi.org/10.1145/3630744.3658408

Goyal, Abhay ; Ho Chun Man, Roger ; Lee, Roy Ka Wei et al. / Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society. editor / Raphael Heiberger ; Ujwal Gadiraju ; Marc Spaniol ; Katharina Kinder-Kurlanda ; Agnieszka Falenska ; Afra Mashhadi ; Jun Sun ; Sierra Kaiser ; Steffen Staab. Association for Computing Machinery, Inc, 2024. pp. 17-18 (Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society).

@inproceedings{98a3efae0d224e25b69391dcd4868a98,

title = "Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care",

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.",

keywords = "Classification, Depression, Primary Care, Voice data",

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

note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 16th ACM Web Science Conference, Websci Companion 2024 ; Conference date: 21-05-2024 Through 24-05-2024",

year = "2024",

month = jun,

day = "13",

doi = "10.1145/3630744.3658408",

language = "English",

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

publisher = "Association for Computing Machinery, Inc",

pages = "17--18",

editor = "Raphael Heiberger and Ujwal Gadiraju and Marc Spaniol and Katharina Kinder-Kurlanda and Agnieszka Falenska and Afra Mashhadi and Jun Sun and Sierra Kaiser and Steffen Staab",

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

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Goyal, A, Ho Chun Man, R, Lee, RKW, Saha, K, L. Altice, F, Poellabauer, C, Papakyriakopoulos, O, Yin Cheung, L, De Choudhury, M, Allagh, K & Kumar, N 2024, Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. in R Heiberger, U Gadiraju, M Spaniol, K Kinder-Kurlanda, A Falenska, A Mashhadi, J Sun, S Kaiser & S Staab (eds), Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society. Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society, Association for Computing Machinery, Inc, pp. 17-18, 16th ACM Web Science Conference, Websci Companion 2024, Stuttgart, Germany, 21/05/24. https://doi.org/10.1145/3630744.3658408

Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. / Goyal, Abhay; Ho Chun Man, Roger; Lee, Roy Ka Wei et al.
Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society. ed. / Raphael Heiberger; Ujwal Gadiraju; Marc Spaniol; Katharina Kinder-Kurlanda; Agnieszka Falenska; Afra Mashhadi; Jun Sun; Sierra Kaiser; Steffen Staab. Association for Computing Machinery, Inc, 2024. p. 17-18 (Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care

AU - Goyal, Abhay

AU - Ho Chun Man, Roger

AU - Lee, Roy Ka Wei

AU - Saha, Koustuv

AU - L. Altice, Frederick

AU - Poellabauer, Christian

AU - Papakyriakopoulos, Orestis

AU - Yin Cheung, Lam

AU - De Choudhury, Munmun

AU - Allagh, Kanica

AU - Kumar, Navin

PY - 2024/6/13

Y1 - 2024/6/13

N2 - 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.

AB - 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.

KW - Classification

KW - Depression

KW - Primary Care

KW - Voice data

UR - https://www.scopus.com/pages/publications/85197168765

U2 - 10.1145/3630744.3658408

DO - 10.1145/3630744.3658408

M3 - Conference contribution

AN - SCOPUS:85197168765

T3 - Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society

SP - 17

EP - 18

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

A2 - Heiberger, Raphael

A2 - Gadiraju, Ujwal

A2 - Spaniol, Marc

A2 - Kinder-Kurlanda, Katharina

A2 - Falenska, Agnieszka

A2 - Mashhadi, Afra

A2 - Sun, Jun

A2 - Kaiser, Sierra

A2 - Staab, Steffen

PB - Association for Computing Machinery, Inc

T2 - 16th ACM Web Science Conference, Websci Companion 2024

Y2 - 21 May 2024 through 24 May 2024

ER -

Goyal A, Ho Chun Man R, Lee RKW, Saha K, L. Altice F, Poellabauer C et al. Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. In Heiberger R, Gadiraju U, Spaniol M, Kinder-Kurlanda K, Falenska A, Mashhadi A, Sun J, Kaiser S, Staab S, editors, Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society. Association for Computing Machinery, Inc. 2024. p. 17-18. (Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society). doi: 10.1145/3630744.3658408

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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