Artificial Intelligence Language Predictors of Two-Year Trauma-Related Outcomes

Details

• Personal Author:
  Bromet EJ ; Clouston SAP ; Kotov R ; Luft BJ ; Miao J ; Oltmanns JR ; Ruggero C ; Schwartz HA ; Son Y ; Waszczuk M
• Description:
  Background: Recent research on artificial intelligence has demonstrated that natural language can be used to provide valid indicators of psychopathology. The present study examined artificial intelligence-based language predictors (ALPs) of seven trauma-related mental and physical health outcomes in responders to the World Trade Center disaster. Methods: The responders (N = 174, Mage = 55.4 years) provided daily voicemail updates over 14 days. Algorithms developed using machine learning in large social media discovery samples were applied to the voicemail transcriptions to derive ALP scores for several risk factors (depressivity, anxiousness, anger proneness, stress, and personality). Responders also completed self-report assessments of these risk factors at baseline and trauma-related mental and physical health outcomes at two-year follow-up (including symptoms of depression, posttraumatic stress disorder, sleep disturbance, respiratory problems, and GERD). Results: Voicemail ALPs were significantly associated with a majority of the trauma-related outcomes at two-year follow-up, over and above corresponding baseline self-reports. ALPs showed significant convergence with corresponding self-report scales, but also considerable uniqueness from each other and from self-report scales. Limitations: The study has a relatively short follow-up period relative to trauma occurrence and a limited sample size. Conclusions: This study shows evidence that ALPs may provide a novel, objective, and clinically useful approach to forecasting, and may in the future help to identify individuals at risk for negative health outcomes. [Description provided by NIOSH]
• Subjects:
  Emergency Responders Mental Health Nervous System Occupational Health Safety September 11 Terrorist Attacks Sleep Sleep Initiation And Maintenance Disorders
• Keywords:
  Algorithms Artificial Intelligence Author Keywords: Assessment Emergency Responders First Responders Mental Health Natural Language Processing Post-traumatic Stress Disorder PTSD Sleep Disorders Trauma World Trade Center WTC

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• ISSN:
  0022-3956
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; New York ; OSHA Region 2 ; OSHA Region 5 ; OSHA Region 6 ; Texas
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  239-245
• Volume:
  143
• NIOSHTIC Number:
  nn:20063915
• Citation:
  J Psychiatr Res 2021 Nov; 143:239-245
• Contact Point Address:
  Joshua R Oltmanns, Department of Psychiatry, Stony Brook University, Stony Brook, New York 11794, USA
• Email:
  joshua.oltmanns@stonybrookmedicine.edu
• Federal Fiscal Year:
  2022
• Performing Organization:
  State University of New York - Stony Brook
• Peer Reviewed:
  True
• Start Date:
  20160901
• Source Full Name:
  Journal of Psychiatric Research
• End Date:
  20210831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:4b251b9e0f693a02d655f567f67281820e7f14ee1d74450bb06a8a41d60f002615010f2cfb009765dedc46034baf5cc9fce8dd2e812fc0d45624e017730fde76
• Download URL:
  https://stacks.cdc.gov/view/cdc/222453/cdc_222453_DS1.pdf
• File Type:
  [PDF - 461.80 KB ]