Suicidal behaviour prediction models using machine learning techniques: A systematic review

Nordin, Noratikah; Zainol, Zurinahni; Noor, Mohd Halim Mohd; Chan, Lai Fong

doi:10.1016/j.artmed.2022.102395

Cited by 23 publications

(8 citation statements)

References 62 publications

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“…No doubt this is because information from all the questionnaires, as well as demographic and criminal justice factors were used by the machine algorithm to make the predictions. The AUC we derived in our validation data is higher than the average reported for machine learning studies generally predicting self-harm (Nordin, Zainol et al 2022). The AUC we observed for our algorithm also compares favourably with that of 0.84 cited by a previous study which developed a risk prediction model for self-harm in male prisoners (Ryland, Gould et al 2020).…”

Section: Discussioncontrasting

confidence: 65%

Predicting self-harm at one year in female prisoners: a retrospective cohort study using machine learning

Tiffin,

Leelamanthep,

Paton

et al. 2023

Preprint

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Background: Self-harm and suicide are relatively overrepresented in incarcerated populations, especially in female prisons. Identifying those most at risk of significant self-harm could provide opportunities for effective, targeted interventions. Aims: To develop and validate a machine learning-based algorithm capable of achieving a clinically useful level of accuracy when predicting the risk of self-harm in female prisoners. Method: Data were available on 31 variables for 286 female prisoners from a single UK-based prison. This included sociodemographic factors, nature of the index offence, and responses to several psychometric assessment tools used at baseline. At 12-month follow-up any self-harm incidents were reported. A machine learning algorithm (CatBoost) to predict self-harm at one-year was developed and tested. To quantify uncertainty about the accuracy of the algorithm, the model building and evaluation process was repeated 2000 times and the distribution of results summarised. Results: The mean Area Under the Curve (AUC) for the model on unseen (validation) data was 0.92 (SD 0.04). Sensitivity was 0.83 (SD 0.07), specificity 0.94 (SD 0.03), positive predictive value 0.78 (SD 0.08) and the negative predictive value 0.95 (0.02). If the algorithm was used in this population, for every 100 women screened, this would equate to approximately 17 true positives and five false positives. Conclusions: The accuracy of the algorithm was superior to those previously reported for predicting future self-harm in general and prison populations and likely to provide clinically useful levels of prediction. Research is needed to evaluate the feasibility of implementing this approach in a prison setting.

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Section: Discussioncontrasting

confidence: 65%

Predicting self-harm at one year in female prisoners: a retrospective cohort study using machine learning

Tiffin,

Leelamanthep,

Paton

et al. 2023

Preprint

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“…7 The WHO also highlighted the need to detect early predictors of suicidal behaviors, which can help to identify subjects at risk, plan prevention strategies and implement specific therapeutic interventions. 1,7 In recent years, artificial intelligence proved to be an effective approach to automating the analysis of medical data and extracting new combinations of biomarkers useful for early diagnosis, [8][9][10][11] and for the identification of suicidal behaviors among psychiatrically hospitalized adolescents. 12 In this scenario, identifying risk factors for progression from suicidal ideation to attempted suicide/failed suicide is one of the main challenges in suicidology.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, artificial intelligence proved to be an effective approach to automating the analysis of medical data and extracting new combinations of biomarkers useful for early diagnosis, 8 – 11 and for the identification of suicidal behaviors among psychiatrically hospitalized adolescents. 12 …”

Section: Introductionmentioning

confidence: 99%

Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents

Servi,

Chiaro,

Mussi

et al. 2023

Science Progress

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Background: Suicidal Behaviors and Thoughts are a relevant public health issue that includes suicidal ideation, non-suicidal self-harm, attempted suicide, and failed suicides. Since there is a progression of suicidal behaviors, whereby suicide is more likely to be completed if there have already been previous behaviors or attempts to harm oneself, WHO has highlighted the need to detect early predictors of such suicidal behaviors, which can help identify individuals at risk, plan prevention strategies and implement specific therapeutic interventions, particularly in young people, thus reducing the number of deaths. This retrospective observational study aimed to identify early predictors of suicidal risk in 237 inpatients admitted for Suicidal Behaviors and Thoughts at Child and Adolescent Psychiatry Emergency Unit of the Meyer Children's Hospital, Florence, Italy. Methods: The study was subdivided into three phases: data collection, statistical analysis, and neural network. For each patient, we collected epidemiological and psychopathological data. We stratified the inpatients into two groups: “suicidal volition patients” and “suicidal motivation patients.” Results: The hospitalization rate for suicidal behaviors and thoughts showed a growing trend from 2016 to 2020 (27.69 to 45.28%). Under 12 years of age, diagnosis of disruptive, impulse-control and conduct disorder, previous specialist care, history of attempted suicide, and intoxication as methods of suicide were statistically correlated to an increased risk of suicidal behaviors. Artificial intelligence, with an accuracy of 86.7%, confirmed these risk factors. Limitations: The most important limitation of the study is its retrospective nature. Conclusions: Our study identifies new early predictors of suicidal risk: age less than 12, diagnosis of disruptive, impulse-control and conduct disorder. In addition, suicidal volition behavior emerges as an important and underestimated risk factor. The use of artificial intelligence methods could be supporting the clinician in assessing suicidal risk.

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“…1 These include Army STARRS, [11][12][13] REACH VET, 5,14,15 the Mental Health Research Network, 16,17 and many more. [18][19][20][21][22][23] Recent research suggests that statistical modeling combined with face-to-face screening outperform either alone. 24 To enable prevention, predictive models must be actualized through tools like CDS.…”

Section: Introductionmentioning

confidence: 99%

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening

Walsh,

Ripperger,

Novak

et al. 2024

Preprint

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Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge.MethodsWe report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented.ResultsFrom August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001).ConclusionsIn this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm.ClinicalTrials.govIdentifier:NCT05312437

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Suicidal behaviour prediction models using machine learning techniques: A systematic review

Cited by 23 publications

References 62 publications

Predicting self-harm at one year in female prisoners: a retrospective cohort study using machine learning

Predicting self-harm at one year in female prisoners: a retrospective cohort study using machine learning

Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening

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