Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study
Orlando Fernandes, Liana Catarina Lima Portugal, Priscila Maria de Oliveira da Fonseca, Débora Christina Muchaluat-Saade, Isabel Antunes David, Arthur Viana Machado, Eliane Volchan, Fátima Cristina Smith Erthal, Mirtes Garcia Pereira, Letícia de Oliveira · 2026
Read Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study by Orlando Fernandes, Liana Catarina Lima Portugal, Priscila Maria de Oliveira da Fonseca, Débora Christina Muchaluat-Saade, Isabel Antunes David, Arthur Viana Machado, Eliane Volchan, Fátima Cristina Smith Erthal, Mirtes Garcia Pereira, Letícia de Oliveira (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13141476.txt.
Description
Orlando Fernandes and co-authors use machine learning to flag suicidal thoughts in university communities in this 2026 study. Depression and such thoughts often occur together, but the thoughts can also appear when mood scores are not high. The team looked for the wider set of factors that separate people with these thoughts from those without.
Their data came from an online survey of 3,828 members of the Brazilian academic community. A learning method that weighs each questionnaire source on its own sorted the two groups with about seventy-eight percent balanced accuracy. Depression scores were strong predictors, as expected. Even so, optimism, loneliness, emotional mistreatment in childhood, and background traits together carried half the weight of the model. The authors argue that screening in academic settings should look beyond depression scores, so at-risk people are not missed.
Catalog record
| CREATOR | Orlando Fernandes, Liana Catarina Lima Portugal, Priscila Maria de Oliveira da Fonseca, Débora Christina Muchaluat-Saade, Isabel Antunes David, Arthur Viana Machado, Eliane Volchan, Fátima Cristina Smith Erthal, Mirtes Garcia Pereira, Letícia de Oliveira |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |