% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Krautz:1043715,
      author       = {Krautz, Agnieszka Ewa and Volkening, Julia and Raue, Janik
                      and Otte, Christian and Eickhoff, Simon B. and Ahlers, Eike
                      and Langner, Jörg},
      title        = {{P}rediction of suicide using web based voice recordings
                      analyzed by artificial intelligence},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2025-03001},
      pages        = {23855},
      year         = {2025},
      abstract     = {The integration of machine learning (ML) and deep learning
                      models in suicide risk assessment has advanced significantly
                      in recent years. In this study, we utilized ML in a
                      case-control design, we predicted completed suicides using
                      publicly available, web-based, real-world voice data, and
                      treating speech as a biomarker. Our model demonstrated high
                      accuracy in distinguishing between individuals who died by
                      suicide and carefully matched controls achieving an area
                      under the curve (AUC) of 0.74. This improved to an AUC of
                      0.85 and an accuracy of $76\%$ when analyzing the subset of
                      individuals who died by suicide within 12 months of the
                      audio recording. The best predictive performance was
                      observed with the Multilayer perceptron model, particularly
                      when using the all Bene, Q + U Bene, and Q + U Raw
                      feature sets—highlighting the importance of combining
                      structured and unstructured paralinguistic features. The
                      findings highlight the critical temporal proximity of voice
                      biomarkers to suicide risk. The model’s robustness is
                      further evidenced by its resilience to perturbations in the
                      analytical pipeline. This is the first study to successfully
                      predict actual suicidal behavior rather than surrogate
                      markers, marking a major step forward in suicide prevention.
                      By demonstrating that speech can serve as a non-invasive and
                      objective biomarker for suicide risk, this research opens
                      new avenues for diagnostic and prognostic applications.},
      cin          = {INM-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {40615574},
      UT           = {WOS:001523063600012},
      doi          = {10.1038/s41598-025-08639-2},
      url          = {https://juser.fz-juelich.de/record/1043715},
}