The use of advanced technology and statistical methods to predict and prevent suicide

EM Kleiman, CR Glenn, RT Liu - Nature reviews psychology, 2023 - nature.com
In the past decade, two themes have emerged across suicide research. First, according to
meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker …

The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review

K Kusuma, M Larsen, JC Quiroz, M Gillies… - Journal of psychiatric …, 2022 - Elsevier
Research has posited that machine learning could improve suicide risk prediction models,
which have traditionally performed poorly. This systematic review and meta-analysis …

[HTML][HTML] Expectations for artificial intelligence (AI) in psychiatry

S Monteith, T Glenn, J Geddes, PC Whybrow… - Current Psychiatry …, 2022 - Springer
Abstract Purpose of Review Artificial intelligence (AI) is often presented as a transformative
technology for clinical medicine even though the current technology maturity of AI is low. The …

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

R Schoedel, F Kunz, M Bergmann… - Journal of Personality …, 2023 - psycnet.apa.org
Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining
people's thoughts, feelings, and behaviors. While situational data were previously difficult to …

Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection

E Petersen, A Feragen, ML da Costa Zemsch… - … Conference on Medical …, 2022 - Springer
Convolutional neural networks have enabled significant improvements in medical image-
based diagnosis. It is, however, increasingly clear that these models are susceptible to …

Sleep problems predict next-day suicidal thinking among adolescents: A multimodal real-time monitoring study following discharge from acute psychiatric care

CR Glenn, EM Kleiman, JC Kearns… - Development and …, 2021 - cambridge.org
Suicidal thoughts and behaviors (STBs) are major public health concerns among
adolescents, and research is needed to identify how risk is conferred over the short term …

[HTML][HTML] The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods

EM McCormick, ML Byrne, JC Flournoy, KL Mills… - Developmental cognitive …, 2023 - Elsevier
Longitudinal data are becoming increasingly available in developmental neuroimaging. To
maximize the promise of this wealth of information on how biology, behavior, and cognition …

Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time

AG Horwitz, SD Kentopp, J Cleary, K Ross… - Psychological …, 2023 - cambridge.org
BackgroundUse of intensive longitudinal methods (eg ecological momentary assessment,
passive sensing) and machine learning (ML) models to predict risk for depression and …

Responsible and regulatory conform machine learning for medicine: a survey of challenges and solutions

E Petersen, Y Potdevin, E Mohammadi… - IEEE …, 2022 - ieeexplore.ieee.org
Machine learning is expected to fuel significant improvements in medical care. To ensure
that fundamental principles such as beneficence, respect for human autonomy, prevention of …

[HTML][HTML] Real-time real-world digital monitoring of adolescent suicide risk during the six months following emergency department discharge: Protocol for an intensive …

S Barzilay, S Fine, S Akhavan… - JMIR research …, 2023 - researchprotocols.org
Background: Suicide is the second leading cause of death in adolescents, and self-harm is
one of the strongest predictors of death by suicide. The rates of adolescents presenting to …