Systematic Suicide Audit: An Enhanced Method to Assess System Gaps and Mobilize Leaders for Prevention

Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models' output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.

Six Major Steps to Make Investigations of Suicide Valuable for Learning and Prevention

OBJECTIVE

The decline in suicide rates has leveled off in many countries during the last decade, suggesting that new interventions are needed in the work with suicide prevention. Learnings from investigations of suicide should contribute to the development of these new interventions. However, reviews of investigations have indicated that few new lessons have been learned. To be an effective tool, revisions of the current investigation methods are required. This review aimed to describe the problems with the current approaches to investigations of suicide as patient harm and to propose ways to move forward.

METHODS

Narrative literature review.

RESULTS

Several weaknesses in the current approaches to investigations were identified. These include failures in embracing patient and system perspectives, not addressing relevant factors, and insufficient competence of the investigation teams. Investigation methods need to encompass the progress of knowledge about suicidal behavior, suicide prevention, and patient safety.

CONCLUSIONS

There is a need for a paradigm shift in the approaches to investigations of suicide as potential patient harm to enable learning and insights valuable for healthcare improvement. Actions to support this paradigm shift include involvement of patients and families, education for investigators, multidisciplinary analysis teams with competence in and access to relevant parts across organizations, and triage of cases for extensive analyses. A new model for the investigation of suicide that support these actions should facilitate this paradigm shift.HIGHLIGHTSThere are weaknesses in the current approaches to investigations of suicide.A paradigm shift in investigations is needed to contribute to a better understanding of suicide.New knowledge of suicidal behavior, prevention, and patient safety must be applied.