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A Machine Learning Approach to Detect Suicidal Ideation in US Veterans Based on Acoustic and Linguistic Features of Speech ...
Abstract: Preventing Veteran suicide is a national priority. The US Department of Veterans Affairs (VA) collects, analyzes, and publishes data to inform suicide prevention strategies. Current approaches for detecting suicidal ideation mostly rely on patient self report which are inadequate and time consuming. In this research study, our goal was to automate suicidal ideation detection from acoustic and linguistic features of an individual's speech using machine learning (ML) algorithms. Using voice data collected from Veterans enrolled in a large interventional study on Gulf War Illness at the Washington DC VA Medical Center, we conducted an evaluation of the performance of different ML approaches in achieving our objective. By fitting both classical ML and deep learning models to the dataset, we identified the algorithms that were most effective for each feature set. Among classical machine learning algorithms, the Support Vector Machine (SVM) trained on acoustic features performed best in classifying suicidal ...
Keyword: Audio and Speech Processing eess.AS; Computers and Society cs.CY; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
URL: https://arxiv.org/abs/2009.09069
https://dx.doi.org/10.48550/arxiv.2009.09069
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2
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
In: Faculty Publications (2019)
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3
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Krishnaprasad Thirunarayan (2019)
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4
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Amit P. Sheth (2019)
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5
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
In: Amit P. Sheth (2019)
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6
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Kno.e.sis Publications (2018)
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7
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Ashutosh Jadhav (2015)
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8
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Ashutosh Jadhav (2015)
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9
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Ashutosh Jadhav (2015)
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10
Analysis of Online Information Searching for Cardiovascular Diseases on a Consumer Health Information Portal
Jadhav, Ashutosh; Sheth, Amit; Pathak, Jyotishman. - : American Medical Informatics Association, 2014
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11
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Publications (2014)
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12
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Publications (2014)
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13
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Publications (2014)
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14
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Kno.e.sis Publications (2014)
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15
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Amit P. Sheth (2014)
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16
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Kno.e.sis Publications (2014)
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17
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Kno.e.sis Publications (2014)
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18
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Amit P. Sheth (2014)
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19
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Amit P. Sheth (2014)
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20
Terminology Representation Guidelines for Biomedical Ontologies in the Semantic Web Notations
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