APA   Click to copy
Grecucci, A., Lapomarda, G., Messina, I., Monachesi, B., Sorella, S., & Siugzdaite, R. (2022). Structural features related to affective instability correctly classify patients with borderline personality disorder. A supervised machine learning approach. Frontiers in Psychiatry, 13, 804440. https://doi.org/10.3389/fpsyt.2022.804440

Chicago/Turabian   Click to copy
Grecucci, Alessandro, Gaia Lapomarda, Irene Messina, Bianca Monachesi, Sara Sorella, and Roma Siugzdaite. “Structural Features Related to Affective Instability Correctly Classify Patients with Borderline Personality Disorder. A Supervised Machine Learning Approach.” Frontiers in Psychiatry 13 (2022): 804440.

MLA   Click to copy
Grecucci, Alessandro, et al. “Structural Features Related to Affective Instability Correctly Classify Patients with Borderline Personality Disorder. A Supervised Machine Learning Approach.” Frontiers in Psychiatry, vol. 13, Frontiers Media SA, 2022, p. 804440, doi:10.3389/fpsyt.2022.804440.

@article{grecucci2022a,
  title = {Structural features related to affective instability correctly classify patients with borderline personality disorder. A supervised machine learning approach},
  year = {2022},
  journal = {Frontiers in Psychiatry},
  pages = {804440},
  publisher = {Frontiers Media SA},
  volume = {13},
  doi = {10.3389/fpsyt.2022.804440},
  author = {Grecucci, Alessandro and Lapomarda, Gaia and Messina, Irene and Monachesi, Bianca and Sorella, Sara and Siugzdaite, Roma}
}