skip to main content
10.1145/2487575.2488196acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

An integrated framework for suicide risk prediction

Published: 11 August 2013 Publication History

Abstract

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient's clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in l1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.

References

[1]
T. Amemiya. Qualitative response models. Annals of Economic and Social Measurement, 4(3):363--372, 1975.
[2]
B. Bondy, A. Buettner, and P. Zill. Genetics of suicide. Molecular psychiatry, 11(4):336--351, 2006.
[3]
Leo Breiman. Bagging predictors. Machine Learning, 24(2):123--140, 1996.
[4]
G.K. Brown, A.T. Beck, R.A. Steer, and J.R. Grisham. Risk factors for suicide in psychiatric outpatients: A 20-year prospective study. Journal of Consulting and Clinical Psychology, 68(3):371, 2000.
[5]
B. Efron and R. Tibshirani. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical science, 1(1):54--75, 1986.
[6]
EJ Gumbel. Statistical of extremes. Columbia University Press, New York, 1958.
[7]
Keith Hawton, Daniel Zahl, and Rosamund Weatherall. Suicide following deliberate self-harm: long-term follow-up of patients who presented to a general hospital. The British Journal of Psychiatry, 182(6):537--542, 2003.
[8]
A.K. Johnston, J.E. Pirkis, and P.M. Burgess. Suicidal thoughts and behaviours among Australian adults: findings from the 2007 National Survey of Mental Health and Wellbeing. Australian and New Zealand Journal of Psychiatry, 43(7):635--643, 2009.
[9]
M. Large and O. Nielssen. Suicide is preventable but not predictable. Australasian Psychiatry, 20(6):532--533, 2012.
[10]
M. Large, C. Ryan, and O. Nielssen. The validity and utility of risk assessment for inpatient suicide. Australasian Psychiatry, 19(6):507--512, 2011.
[11]
M.M. Large and O.B. Nielssen. Suicide in Australia: meta-analysis of rates and methods of suicide between 1988 and 2007. Medical Journal of Australia, 192(8):432--437, 2010.
[12]
P. McCullagh. Regression models for ordinal data. Journal of the Royal Statistical Society. Series B (Methodological), pages 109--142, 1980.
[13]
P. McCullagh and J.A. Nelder. Generalized linear models. Chapman & HallCRC, 1989.
[14]
D. McFadden. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics, pages 105--142, 1973.
[15]
N. Meinshausen and P. Bühlmann. Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4):417--473, 2010.
[16]
G.E. Murphy. The prediction of suicide: Why is it so difficult? American Journal of Psychotherapy, 1984.
[17]
F. Ruiz, I. Valera, C. Blanco, and F. Perez-Cruz. Bayesian nonparametric modeling of suicide attempts. In NIPS, 2012.
[18]
C. Ryan, O. Nielssen, M. Paton, and M. Large. Clinical decisions in psychiatry should not be based on risk assessment. Australasian Psychiatry, 18(5):398--403, 2010.
[19]
R. Tibshirani. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267--288, 1996.
[20]
G. Tutz. Sequential models in categorical regression. Computational Statistics & Data Analysis, 11(3):275--295, 1991.
[21]
F. Wang, N. Lee, J. Hu, J. Sun, and S. Ebadollahi. Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach. In Proc. of the 18th SIGKDD, pages 453--461. ACM, 2012.

Cited By

View all
  • (2025)Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotationJournal of Biomedical Informatics10.1016/j.jbi.2024.104755161(104755)Online publication date: Jan-2025
  • (2024)Analysing Workplace Mental Health Programs Using Data Science: A Comprehensive StudySSRN Electronic Journal10.2139/ssrn.4814209Online publication date: 2024
  • (2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
August 2013
1534 pages
ISBN:9781450321747
DOI:10.1145/2487575
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. filter bank
  2. machine learning
  3. medical data analysis
  4. one-sided convolutional kernels
  5. risk modelling
  6. risk prediction
  7. suicide

Qualifiers

  • Research-article

Conference

KDD' 13
Sponsor:

Acceptance Rates

KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)47
  • Downloads (Last 6 weeks)7
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotationJournal of Biomedical Informatics10.1016/j.jbi.2024.104755161(104755)Online publication date: Jan-2025
  • (2024)Analysing Workplace Mental Health Programs Using Data Science: A Comprehensive StudySSRN Electronic Journal10.2139/ssrn.4814209Online publication date: 2024
  • (2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
  • (2024)Hybrid Text Representation for Explainable Suicide Risk Identification on Social MediaIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318498411:4(4663-4672)Online publication date: Aug-2024
  • (2024)Decoding depression: Analyzing social network insights for depression severity assessment with transformers and explainable AINatural Language Processing Journal10.1016/j.nlp.2024.1000797(100079)Online publication date: Jun-2024
  • (2023)A Content-adaptive Visibility Predictor for Perceptually Optimized Image BlendingACM Transactions on Applied Perception10.1145/356597220:1(1-29)Online publication date: 11-Jan-2023
  • (2023)Facilitating Serverless Match-based Online Games with Novel Blockchain TechnologiesACM Transactions on Internet Technology10.1145/356588423:1(1-26)Online publication date: 23-Feb-2023
  • (2023)WPAD: Waiting Patiently for an Announced DisasterACM Computing Surveys10.1145/356536155:10(1-29)Online publication date: 2-Feb-2023
  • (2023)Designing Human-centered AI for Mental Health: Developing Clinically Relevant Applications for Online CBT TreatmentACM Transactions on Computer-Human Interaction10.1145/356475230:2(1-50)Online publication date: 17-Mar-2023
  • (2023)A Solicitous Approach to Smart Contract VerificationACM Transactions on Privacy and Security10.1145/356469926:2(1-28)Online publication date: 13-Mar-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media