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.
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.
Authors: M J Shaw; N J Talley; T J Beebe; T Rockwood; R Carlsson; S Adlis; A M Fendrick; R Jones; J Dent; P Bytzer Journal: Am J Gastroenterol Date: 2001-01 Impact factor: 10.864
Authors: Christopher R Dasaro; William L Holden; Karen D Berman; Michael A Crane; Julia R Kaplan; Roberto G Lucchini; Benjamin J Luft; Jacqueline M Moline; Susan L Teitelbaum; Usha S Tirunagari; Iris G Udasin; Jean H Weiner; Patrice A Zigrossi; Andrew C Todd Journal: Int J Epidemiol Date: 2017-04-01 Impact factor: 7.196
Authors: David Watson; Michael W O'Hara; Kristin Naragon-Gainey; Erin Koffel; Michael Chmielewski; Roman Kotov; Sara M Stasik; Camilo J Ruggero Journal: Assessment Date: 2012-07-20