Predicting Risk of Suicide Attempts Over Time Through Machine Learning

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A tutorial on calibration measurements and calibration models for clinical prediction models (2020, methodology): “In a relatively recent article from the biomedical informatics literature, Walsh et al 31,32 re-emphasize the calls from Van Calster et al 33 and Riley et al 34 on the importance of calibration in addition to discrimination when evaluating predictive models.”
Supervised machine learning: A brief primer (2020, methodology): “There are many recent applications of recursive partitioning in psychology and psychiatry (Gradus et al., 2020; Walsh et al., 2017).”
Ethics and governance of trustworthy medical artificial intelligence (2023, unknown): “Deep learning is widely used in medical AI and performs very well currently in medical imaging analysis [36] and clinical risk prediction [37] significantly improving doctors’ diagnostic and predictive capabilities.”
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention (2019, methodology): “They achieved an F1-score of 86% with a recall of 95% [61].”