Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.
- DOI
- 10.1016/j.schres.2024.05.011
- Published
- 2024 Aug
- Container
- Schizophrenia research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.schres.2024.05.011,
title = {Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.},
author = {Barruel D and Hilbey J and Charlet J and Chaumette B and Krebs MO and Dauriac-Le Masson V},
year = {2024},
journal = {Schizophrenia research},
doi = {10.1016/j.schres.2024.05.011},
url = {https://doi.org/10.1016/j.schres.2024.05.011}
}RIS
TY - JOUR TI - Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features. AU - Barruel D AU - Hilbey J AU - Charlet J AU - Chaumette B AU - Krebs MO AU - Dauriac-Le Masson V PY - 2024 JO - Schizophrenia research DO - 10.1016/j.schres.2024.05.011 UR - https://doi.org/10.1016/j.schres.2024.05.011 ER -
APA
D, B., J, H., J, C., B, C., MO, K., & V, D. M. (2024). Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.. Schizophrenia research. https://doi.org/10.1016/j.schres.2024.05.011
Source records
- pubmed · retrieved 2026-09-26T17:35:54.376Z