Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning
- DOI
- 10.1038/s41598-024-61557-7
- Published
- 2024-05-30
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-024-61557-7,
title = {Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning},
author = {Robin Quillivic and Frédérique Gayraud and Yann Auxéméry and Laurent Vanni and Denis Peschanski and Francis Eustache and Jacques Dayan and Salma Mesmoudi},
year = {2024},
journal = {Scientific Reports},
doi = {10.1038/s41598-024-61557-7},
url = {https://doi.org/10.1038/s41598-024-61557-7}
}RIS
TY - JOUR TI - Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning AU - Robin Quillivic AU - Frédérique Gayraud AU - Yann Auxéméry AU - Laurent Vanni AU - Denis Peschanski AU - Francis Eustache AU - Jacques Dayan AU - Salma Mesmoudi PY - 2024 JO - Scientific Reports DO - 10.1038/s41598-024-61557-7 UR - https://doi.org/10.1038/s41598-024-61557-7 ER -
APA
Quillivic, R., Gayraud, F., Auxéméry, Y., Vanni, L., Peschanski, D., Eustache, F., Dayan, J., & Mesmoudi, S. (2024). Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning. Scientific Reports. https://doi.org/10.1038/s41598-024-61557-7
Source records
- crossref · retrieved 2026-09-26T16:43:23.442Z