How accurately can supervised machine learning model predict a targeted psychiatric disorder?
Study findings strongly suggest that ML can, in the future, play a significant role in the risk assessment of psychiatric disorders prior to face-to-face consultation. By using AI to scan big data questionnaire responses, wait time for seriously ill patients can be substantially cut, and prognoses substantially improved.
Authors
- Zahra Saif (ORCID: https://orcid.org/0000-0003-2749-3274)
- Michael V. Vitiello (ORCID: https://orcid.org/0000-0002-9776-0473)
- Waqar Husain (ORCID: https://orcid.org/0000-0003-3047-1834)
- Amir H. Pakpour (ORCID: https://orcid.org/0000-0002-8798-5345)
- Adel Aloffi
- Khaled Trabelsi (ORCID: https://orcid.org/0000-0003-2623-9557)
- Ali Alsalman
- Seithikurippu R. Pandi‐Perumal (ORCID: https://orcid.org/0000-0002-8686-7259)
- Haitham Jahrami (ORCID: https://orcid.org/0000-0001-8990-1320)
- Achraf Ammar (ORCID: https://orcid.org/0000-0003-0347-8053)
Institutions
- Chandigarh University (IN)
- Lovely Professional University (IN)
- University of Sfax (TN)
- COMSATS University Islamabad (PK)
- Johannes Gutenberg University Mainz (DE)
- University of Washington (US)
- International Hospital of Bahrain (BH)
- Arabian Gulf University (BH)
- Seattle University (US)
- Jönköping University (SE)
Publication Details
- Journal
- BMC Psychiatry
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1186/s12888-024-06152-w
- Citations
- 1
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 0.51
Funders
- University of Toronto