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Explainable Machine Learning Models for Suicidal Behavior Prediction
We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed.
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with a significant challenge: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods—machine learning (ML) and pattern recognition models in particular—so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions. From an organization viewpoint, after motivating the area broadly, we discuss the main developments, including the principles that allow us to study transparent models vs. opaque models, as well as model-specific or model-agnostic post-hoc explainability approaches. We also briefly reflect on deep learning models, and conclude with a discussion about future research directions.
Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts.
Objectives
Explore international consensus on nomenclatures of suicidal behaviours and analyse differences in terminology between high-income countries (HICs) and low/middle-income countries (LMICs).
Design
An online survey of members of the International Organisation for Suicide Prevention (IASP) used multiple-choice questions and vignettes to assess the four dimensions of the definition of suicidal behaviour: outcome, intent, knowledge and agency.
Setting
International.
Participants
Respondents included 126 individuals, 37 from 30 LMICs and 89 from 33 HICs. They included 40 IASP national representatives (65% response rate), IASP regular members (20% response rate) and six respondents from six additional countries identified by other organisations.
Outcome measures
Definitions of English-language terms for suicidal behaviours.
Results
The recommended definition of ‘suicide’ describes a fatal act initiated and carried out by the actors themselves. The definition of ‘suicide attempt’ was restricted to non-fatal acts with intent to die, whereas definition of ‘self-harm’ more broadly referred to acts with varying motives, including the wish to die. Almost all respondents agreed about the definitions of ‘suicidal ideation’, ‘death wishes’ and ‘suicide plan’. ‘Aborted suicide attempt’ and ‘interrupted suicide attempt’ were not considered components of ‘preparatory suicidal behaviour’. There were several differences between representatives from HICs and LMICs.
Conclusion
This international opinion survey provided the basis for developing a transcultural nomenclature of suicidal behaviour. Future developments of this nomenclature should be tested in larger samples of professionals, including LMICs may be a challenge.
Background
A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making regarding a service response in terms of more detailed assessments and/or intervention. The aim of this study was to predict self-harm within six-months after initial presentation.
Method
The study included 1962 young people (12–30 years) presenting to youth mental health services in Australia. Six machine learning algorithms were trained and tested with ten repeats of ten-fold cross-validation. The net benefit of these models were evaluated using decision curve analysis.
Results
Out of 1962 young people, 320 (16%) engaged in self-harm in the six months after first assessment and 1642 (84%) did not. The top 25% of young people as ranked by mean predicted probability accounted for 51.6% - 56.2% of all who engaged in self-harm. By the top 50%, this increased to 82.1%-84.4%. Models demonstrated fair overall prediction (AUROCs; 0.744–0.755) and calibration which indicates that predicted probabilities were close to the true probabilities (brier scores; 0.185–0.196). The net benefit of these models were positive and superior to the ‘treat everyone’ strategy. The strongest predictors were (in ranked order); a history of self-harm, age, social and occupational functioning, sex, bipolar disorder, psychosis-like experiences, treatment with antipsychotics, and a history of suicide ideation.
Conclusion
Prediction models for self-harm may have utility to identify a large sub population who would benefit from further assessment and targeted (low intensity) interventions. Such models could enhance health service approaches to identify and reduce self-harm, a considerable source of distress, morbidity, ongoing health care utilisation and mortality.
Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients’ historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
Objective:
Suicidal ideation (SI) precedes actual suicidal event. Thus, it is important for the prevention of suicide to screen the individuals with SI. This study aimed to identify the factors associated with SI and to build prediction models in Korean adults using machine learning methods.
Methods:
The 2010-2013 dataset of the Korea National Health and Nutritional Examination Survey was used as the training dataset (n=16,437), and the subset collected in 2015 was used as the testing dataset (n=3,788). Various machine learning algorithms were applied and compared to the conventional logistic regression (LR)-based model.
Results:
Common risk factors for SI included stress awareness, experience of continuous depressive mood, EQ-5D score, depressive disorder, household income, educational status, alcohol abuse, and unmet medical service needs. The prediction performances of the machine learning models, as measured by the area under receiver-operating curve, ranged from 0.794 to 0.877, some of which were better than that of the conventional LR model (0.867). The Bayesian network, LogitBoost with LR, and ANN models outperformed the conventional LR model.
Conclusion:
A machine learning-based approach could provide better SI prediction performance compared to a conventional LR-based model. These may help primary care physicians to identify patients at risk of SI and will facilitate the early prevention of suicide.
Objective
Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictors of future suicidal behavior. The aim of this study was to develop prediction model to identify Korean adolescents of high risk suicide (= who have history of suicide ideation/attempt in previous year) using machine learning techniques.
Methods
A nationally representative dataset of Korea Youth Risk Behavior Web-based Survey (KYRBWS) was used (n = 59,984 of middle and high school students in 2017). The classification process was performed using machine learning techniques such as logistic regression (LR), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGB).
Results
A total of 7,443 adolescents (12.4%) had a previous history of suicidal ideation/attempt. In the multivariable analysis, sadness (odds ratio [OR], 6.41; 95% confidence interval [95% CI], 6.08–6.87), violence (OR, 2.32; 95% CI, 2.01–2.67), substance use (OR, 1.93; 95% CI, 1.52–2.45), and stress (OR, 1.63; 95% CI, 1.40–1.86) were associated factors. Taking into account 26 variables as predictors, the accuracy of models of machine learning techniques to predict the high-risk suicidal was comparable with that of LR; the accuracy was best in XGB (79.0%), followed by SVM (78.7%), LR (77.9%), RF (77.8%), and ANN (77.5%).
Conclusions
The machine leaning techniques showed comparable performance with LR to classify adolescents who have previous history of suicidal ideation/attempt. This model will hopefully serve as a foundation for decreasing future suicides as it enables early identification of adolescents at risk of suicide and modification of risk factors.
Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
Objective::
To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis.
Methods::
A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique.
Results::
The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one's own capacities and coping abilities.
Conclusion::
These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.
Background
We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM).
Methods
We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated.
Results
Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively.
Conclusion
SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN.
The causes of suicidal behaviour are not fully understood; however, this behaviour clearly results from the complex interaction of many factors. Although many risk factors have been identified, they mostly do not account for why people try to end their lives. In this Review, we describe key recent developments in theoretical, clinical, and empirical psychological science about the emergence of suicidal thoughts and behaviours, and emphasise the central importance of psychological factors. Personality and individual differences, cognitive factors, social aspects, and negative life events are key contributors to suicidal behaviour. Most people struggling with suicidal thoughts and behaviours do not receive treatment. Some evidence suggests that different forms of cognitive and behavioural therapies can reduce the risk of suicide reattempt, but hardly any evidence about factors that protect against suicide is available. The development of innovative psychological and psychosocial treatments needs urgent attention.
Background: Depressed inpatients constitute a high-risk population for suicide attempts. Aims: To describe the interactions of clinical and psychosocial risk factors influencing suicide attempts among a Malaysian sample of depressed inpatients. Methods: Seventy-five subjects were diagnosed with a depressive disorder according to the Structured Clinical Interview for DSM-IV Axis I Disorders-Clinical Version (SCID-CV). Data on suicide attempts, suicidal ideation (Scale for Suicidal Ideation, SSI), depression severity (Beck’s Depression Inventory, BDI), recent life-event changes (Social Readjustment Rating Scale, SRRS), sociodemographic and other relevant clinical factors were collected. Results: A third of the subjects presented after a current suicide attempt. Significant factors for a current suicide attempt were race, religion, recent life-event changes, suicidal ideation, and alcohol use disorder. Independent predictive risk factors for a current suicide attempt were Chinese race, recent marital separation, major mortgage or loans, and being newly diagnosed with depression. Any recent change in personal habits was shown to be a protective factor against current suicide attempt. Age and gender were nonsignificant factors. Conclusions: The findings are generally consistent with existing studies and highlight the role of psychosocial risk factors.
Individuals with psychiatric disorders are vulnerable to adverse mental health outcomes following physical illness. This longitudinal cohort study defined risk profiles for readmission for suicidal behavior and self-harm after general hospitalization of adults with serious mental illness. Structured electronic health record data were analyzed from 15,644 general non-psychiatric index hospitalizations of individuals with depression, bipolar, and psychotic disorders admitted to an urban health system in the southwestern United States between 2006 and 2017. Using data from one-year prior to and including index hospitalization, supervised machine learning was implemented to predict risk of readmission for suicide attempt and self-harm in the following year. The Classification and Regression Tree algorithm produced a classification prediction with an area under the receiver operating curve (AUC) of 0.86 (95% confidence interval (CI) 0.74–0.97). Incidence of suicide-related behavior was highest after general non-psychiatric hospitalizations of individuals with prior suicide attempt or self-harm (18%; 69 cases/389 hospitalizations) and lowest after hospitalizations associated with very high medical morbidity burden (0 cases/3090 hospitalizations). Predictor combinations, rather than single risk factors, explained the majority of risk, including concomitant alcohol use disorder with moderate medical morbidity, and age ≤55-years-old with low medical morbidity. Findings suggest that applying an efficient and highly interpretable machine learning algorithm to electronic health record data may inform general hospital clinical decision support, resource allocation, and preventative interventions for medically ill adults with serious mental illness.
Identifying factors that predict who may be at risk of suicide could help prevent suicides via targeted interventions. It is difficult at present, however, to predict which individuals are likely to attempt suicide, even in high risk populations such as Borderline Personality
Disorder (BPD) sufferers. The complexity of person-situation dynamics means that relying on known risk factors may not yield accurate enough results for prevention strategies to be successful. Furthermore, risk models typically rely on suicidal thoughts, even though it has been shown that people often intentionally withhold this information. To address these challenges, this study compared the performance of six machine learning and categorisation models in terms of accurately identifying suicidal behaviour in a prison population (n = 353), by including or excluding questions about previous suicide attempts and suicidal ideation. Results revealed that modern machine learning algorithms, specially gradient tree boosting (AUC = .875, F1 = .846), can accurately identify individuals with suicidal behaviour, even without relying on questions about suicidal thoughts, and this accuracy can be maintained with as low as 29 risk factors. Additionally, based on this evidence, it may be possible to implement a decision tree model using known predictors to assess individuals at risk of suicide. These findings highlight that modern classification algorithms do not necessarily require information about suicidal thoughts for modelling suicide and self harm behaviour.
Background: Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thoughts and behaviors (SITBs). Method: We systematically searched PsycINFO, PsycARTICLES, ERIC, CINAHL, and MEDLINE for articles published through February 2018. Results: Thirty-five articles met criteria to be included in the review. Included articles were reviewed by outcome: suicide death, suicide attempt, suicide plan, suicidal ideation, suicide risk, and non-suicidal self-injury. We observed three general aims in the use of SITB-focused machine learning analyses: (1) improving prediction accuracy, (2) identifying important model indicators (i.e., variable selection) and indicator interactions, and (3) modeling underlying subgroups. For studies with the aim of boosting predictive accuracy, we observed greater prediction accuracy of SITBs than in previous studies using traditional statistical methods. Studies using machine learning for variable selection purposes have both replicated findings of well-known SITB risk factors and identified novel variables that may augment model performance. Finally, some of these studies have allowed for subgroup identification, which in turn has helped to inform clinical cutoffs. Limitations: Limitations of the current review include relatively low paper sample size, inconsistent reporting procedures resulting in an inability to compare model accuracy across studies, and lack of model validation on external samples. Conclusions: We concluded that leveraging machine learning techniques to further predictive accuracy and identify novel indicators will aid in the prediction and prevention of suicide.
Data preprocessing techniques generally refer to the addition, deletion, or transformation of the training set data. Preprocessing data is a crucial step prior to modeling since data preparation can make or break a model’s predictive ability. To illustrate general preprocessing techniques, we begin by introducing a cell segmentation data set (Section 3.1). This data set contains common predictor problems such as skewness, outliers, and missing values. Sections 3.2 and 3.3 review predictor transformations for single predictors and multiple predictors, respectively. In Section 3.4 we discuss several approaches for handling missing data. Other preprocessing steps may include removing (Section 3.5), adding (Section 3.6), or binning (Section 3.7) predictors, all of which must be done carefully so that predictive information is not lost or erroneous information is added to the data. The computing section (3.8) provides R syntax for the previously described preprocessing steps. Exercises are provided at the end of the chapter to solidify concepts.
When predicting a categorical outcome, some measure of classification accuracy is typically used to evaluate the model’s effectiveness. However, there are different ways to measure classification accuracy, depending of the modeler’s primary objectives. Most classification models can produce both a continuous and categorical prediction output. In Section 11.1, we review these outputs, demonstrate how to adjust probabilities based on calibration plots, recommend ways for displaying class predictions, and define equivocal or indeterminate zones of prediction. In Section 11.2, we review common metrics for assessing classification predictions such as accuracy, kappa, sensitivity, specificity, and positive and negative predicted values. This section also addresses model evaluation when costs are applied to making false positive or false negative mistakes. Classification models may also produce predicted classification probabilities. Evaluating this type of output is addressed in Section 11.3, and includes a discussion of receiver operating characteristic curves as well as lift charts. In Section 11.4, we demonstrate how measures of classification performance can be generated in R.
When predicting a numeric outcome, some measure of accuracy is typically used to evaluate the model’s effectiveness. However, there are different ways to measure accuracy, each with its own nuance. In Section 5.1 we define common measures for evaluating quantitative performance. We also discuss the concept of variance-bias trade-off (Section 5.2), and the implication of this principle for predictive modeling. In Section 5.3, we demonstrate how measures of predictive performance can be generated in R.
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. © Springer Science+Business Media New York 2013. All rights reserved.
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