Mining Free-Text Medical Notes for Suicide Risk Assessment
Abstract
Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.
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Since the sample cohort was unbalanced for PSH and FSH across all sites, the authors used the weighted average from scikit learn python library to compute precision, recall and F1-score.
“Since the sample cohort was unbalanced for PSH and FSH across all sites, we used the weighted average from scikit learn python library to compute these measures36.”
From Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs (2024) · cites this paper as [36]
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Bias in error estimation when using cross-validation for model selection
Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485 · cited 1× · see citing works
“It has been shown, both theoretically as well as empirically [28–30] that the cross-validated estimation of performance of the best classifier is optimistic.”
BackgroundKessler, R. C., Warner, C. H., Ivany, C., Petukhova, M. V., Rose, S., Bromet, E. J., Brown, M., Cai, T., Colpe, L. J., Cox, K. L., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1754 · Cited by 447 · see citing works
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Natural language processing applied to mental illness detection: a narrative review
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476 · see citing works
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406 · see citing works
Petsolari, M., Ibrahim, S. B., & Slovak, P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642619 · Cited by 35 · see citing works
Adekkanattu, P., Furmanchuk, A., Wu, Y., Pathak, A., Patra, B. G., Bost, S., Morrow, D., Wang, G. H.-M., Yang, Y., Forrest, N. J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01266-7 · Cited by 12 · see citing works
- Authors
- Adamou, M., Antoniou, G., Greasidou, E., Lagani, V., Charonyktakis, P., & Tsamardinos, I.
- Affiliations
- University of Huddersfield (GB)
- Gnosis Data Analysis PC, Greece
- University of Crete (GR)
- Venue
- Proceedings of the 10th Hellenic Conference on Artificial Intelligence
- Publisher
- ACM
- Published
- 2018-07-09
- Pages
- 1-8
- OpenAlex
- W2850540329
- Cited by
- 22
- References
- 2
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 4 · 2018: 1
- FWCI
- 3.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Hellenic Conference on Artificial Intelligence (publishedVersion)
- Huddersfield Research Portal (University of Huddersfield) (submittedVersion)
- Griffith Research Online (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- suicide risk assessment, suicide risk prediction, clinical notes, text mining, suicide prevention, mental health services, referral, structured clinical data
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