Outpatient Engagement and Predicted Risk of Suicide Attempts in Fibromyalgia

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

OBJECTIVE

Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.

METHOD

A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.

RESULTS

Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.

CONCLUSIONS

The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Cross-sectional study of psychiatric disorders in patients with chronic musculoskeletal pain and individuals without pain

BACKGROUND

Musculoskeletal chronic pain is a leading cause of global disability and laboral incapacity. However, there is a lack of population-based studies that investigate the relationship between chronic pain and mental disorders with a control group, particularly among low- and middle-income countries. Chronic pain is a serious public health problem in terms of human suffering, and in terms of socioeconomic implications. Frequent association with different mental disorders increases disability, decreases quality of life, and makes diagnosis and treatment challenging. The present study aimed to evaluate the presence of mental disorders in patients with chronic musculoskeletal pain and compare with a control group without pain.

METHODS

We selected 100 patients in a regular follow-up at the Musculoskeletal Pain Outpatient Clinic of the University Hospital and compared them with 100 painless individuals from the control group from June 2016 to June 2018. The instruments used were the Mini International Neuropsychiatric Interview (MINI-PLUS) and a structured questionnaire to collect sociodemographic data. Statistical analysis used t-test, chi-square, Fisher's exact test, Mann-Whitney, Kolmogorov-Smirnov tests, and multiple logistic regression.

RESULTS

In the sample evaluated, the majority of patients were women (83%), of brown color (54%), with lower-level education (51%), lower salary range (73%) and high absenteeism rate at work (60,7%). Patients with chronic pain had more psychiatric disorders (88% vs. 48% in the control group; p < 0.001). The most frequent diagnoses were anxiety disorders with panic attacks (44%), generalized anxiety (36%), mixed anxiety and depression disorder (33%), social phobia (30%), agoraphobia (29%), suicide risk (28%), and major depression (27%).

CONCLUSION

Positive correlations of mental disorders and chronic musculoskeletal pain have been documented. This suggests that psychiatric components must be taken into account in the management of chronic pain syndromes. The use of Mini Plus as a diagnostic tool for psychiatric disorders can contribute to optimizing the diagnosis and treatment of patients with chronic pain and encourage the creation of policies with strategies and criteria for quick access to Multi-professional Services.

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

Background

Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature.

Objective

Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified.

Methods

A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed.

Results

The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study.

Conclusion

The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Identifying Predictors of Psychological Problems Among Adolescents With Congenital Heart Disease for Referral to Psychological Care: A Pilot Study

Background

The lifelong care of patients with congenital heart disease (CHD) typically begins at a young age, giving paediatric cardiologists a unique perspective on the mental health of their patients. Our aim was to describe and predict reported psychological problems among adolescents with CHD.

Methods

A retrospective review was performed on patients aged 12-17 years who presented to the congenital cardiology clinic during a 1-year timeframe. The presence of psychological problems was collected along with CHD class, clinical history, developmental delay, and patient demographics. We described the prevalence of psychological problems and then, using machine learning algorithms, trained and tested optimal predictive models.

Results

Of the 397 patients who met inclusion criteria, the lifetime prevalence of any reported psychological problem was 35.5%. The most prevalent reported problems were attention-deficit/hyperactivity disorder (18.9%), anxiety (17.6%), and depression (16.1%). Contrary to our expectations, we could not predict the presence or absence of any psychological problem using routine clinical data. Instead, we found multivariate models predicting depression and attention-deficit/hyperactivity disorder with promising accuracy. Prediction of anxiety was less successful.

Conclusions

Approximately 1 of 3 adolescents with CHD presented with the lifetime prevalence of 1 or more psychological problems. Congenital cardiac programmes are in a position of influence to respond to these problems and impact their patients' mental health as part of a comprehensive care plan. The discovered models using routine clinical data predicted specific psychological problems with varying accuracy. With further validation, these models could become the tools of routine recommendations for referral to psychological care.

Prevalence and Characterization of Psychological Trauma in Patients with Fibromyalgia: A Cross-Sectional Study

Background

Preliminary evidence suggests that psychological trauma, especially childhood trauma, is a risk factor for the onset of fibromyalgia (FM).

Objective

The main objective of this study consisted of evaluating the prevalence and detailed characteristics of psychological trauma in a sample of patients with FM, the chronology of trauma across the lifespan, and its clinical symptoms. We also calculated whether childhood trauma could predict the relationship with different clinical variables.

Method

Eighty-eight females underwent an interview to assess sociodemographic data, psychiatric comorbidities, level of pain, FM impact, clinical symptoms of anxiety, depression, insomnia, quality of life, and psychological trauma.

Results

The majority of participants (71.5%) met the diagnostic criteria for current post-traumatic stress disorder (PTSD). Participants reported having suffered traumatic events throughout their lifespan, especially in childhood and early adolescence, in the form of emotional abuse, emotional neglect, sexual abuse, and physical abuse. Traumatic events predict both poor quality of life and a level of pain in adulthood. All patients showed clinically relevant levels of anxiety, depression, insomnia, suicidal thoughts, and pain, as well as somatic comorbidities and poor quality of life. Pain levels predicted anxiety, depression, dissociation, and insomnia symptoms. 84% of the sample suffered one or more traumatic events prior to the onset of pain.

Conclusions

Our data highlight the clinical complexity of patients with FM and the role of childhood trauma in the onset and maintenance of FM, as well as the high comorbidity between anxiety, depression, somatic symptoms, and FM. Our data also supports FM patients experiencing further retraumatization as they age, with an extremely high prevalence of current PTSD in our sample. These findings underscore the need for multidisciplinary programs for FM patients to address their physical pain and their psychiatric and somatic conditions, pay special attention to the assessment of psychological trauma, and provide trauma-focused interventions. Trial registration: ClinicalTrials.gov NCT04476316. Registered on July 20th, 2020.

Predictive Modeling for Suicide-Related Outcomes and Risk Factors among Patients with Pain Conditions: A Systematic Review

Suicide is a leading cause of death in the US. Patients with pain conditions have higher suicidal risks. In a systematic review searching observational studies from multiple sources (e.g., MEDLINE) from 1 January 2000–12 September 2020, we evaluated existing suicide prediction models’ (SPMs) performance and identified risk factors and their derived data sources among patients with pain conditions. The suicide-related outcomes included suicidal ideation, suicide attempts, suicide deaths, and suicide behaviors. Among the 87 studies included (with 8 SPM studies), 107 suicide risk factors (grouped into 27 categories) were identified. The most frequently occurring risk factor category was depression and their severity (33%). Approximately 20% of the risk factor categories would require identification from data sources beyond structured data (e.g., clinical notes). For 8 SPM studies (only 2 performing validation), the reported prediction metrics/performance varied: C-statistics (n = 3 studies) ranged 0.67–0.84, overall accuracy(n = 5): 0.78–0.96, sensitivity(n = 2): 0.65–0.91, and positive predictive values(n = 3): 0.01–0.43. Using the modified Quality in Prognosis Studies tool to assess the risk of biases, four SPM studies had moderate-to-high risk of biases. This systematic review identified a comprehensive list of risk factors that may improve predicting suicidal risks for patients with pain conditions. Future studies need to examine reasons for performance variations and SPM’s clinical utility.

A machine learning approach to identification of self-harm and suicidal ideation among military and police Veterans

LAY SUMMARY

Combat Veterans are vulnerable to suicidal thoughts and behaviour. Many who die by suicide deny having suicidal ideation (SI). Typically, researchers try to find variables indicating the presence of SI using traditional statistical approaches. These approaches do not possess the capacity to detect highly complex multivariable interactions. In contrast, machine learning (ML) is designed to detect such patterns and can consequently yield much higher predictive accuracy. In this study, the authors trained ML algorithms using 192 variables extracted from questionnaires administered to 738 Veterans and serving personnel to detect the presence of self-harm and SI (SHSI). Using the 10 most predictive non-suicide-related items, the ML algorithms could detect SHSI with 75.3% accuracy. Most of these items reflect psychological phenomena that can change quickly over time, allowing repeated risk reassessment from day to day. The study’s findings suggest that ML methods may play an important role in the discovery, within a large data set, of predictive patterns that might be useful in suicide risk assessment.

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

Prospective Validation of an Electronic Health Record-Based, Real-Time Suicide Risk Model

IMPORTANCE

Numerous prognostic models of suicide risk have been published, but few have been implemented outside of integrated managed care systems.

OBJECTIVE

To evaluate performance of a suicide attempt risk prediction model implemented in a vendor-supplied electronic health record to predict subsequent (1) suicidal ideation and (2) suicide attempt.

DESIGN, SETTING, AND PARTICIPANTS

This observational cohort study evaluated implementation of a suicide attempt prediction model in live clinical systems without alerting. The cohort comprised patients seen for any reason in adult inpatient, emergency department, and ambulatory surgery settings at an academic medical center in the mid-South from June 2019 to April 2020.

MAIN OUTCOMES AND MEASURES

Primary measures assessed external, prospective, and concurrent validity. Manual medical record validation of coded suicide attempts confirmed incident behaviors with intent to die. Subgroup analyses were performed based on demographic characteristics, relevant clinical context/setting, and presence or absence of universal screening. Performance was evaluated using discrimination (number needed to screen, C statistics, positive/negative predictive values) and calibration (Spiegelhalter z statistic). Recalibration was performed with logistic calibration.

RESULTS

The system generated 115 905 predictions for 77 973 patients (42 490 [54%] men, 35 404 [45%] women, 60 586 [78%] White, 12 620 [16%] Black). Numbers needed to screen in highest risk quantiles were 23 and 271 for suicidal ideation and attempt, respectively. Performance was maintained across demographic subgroups. Numbers needed to screen for suicide attempt by sex were 256 for men and 323 for women; and by race: 373, 176, and 407 for White, Black, and non-White/non-Black patients, respectively. Model C statistics were, across the health system: 0.836 (95% CI, 0.836-0.837); adult hospital: 0.77 (95% CI, 0.77-0.772); emergency department: 0.778 (95% CI, 0.777-0.778); psychiatry inpatient settings: 0.634 (95% CI, 0.633-0.636). Predictions were initially miscalibrated (Spiegelhalter z = -3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26).

CONCLUSIONS AND RELEVANCE

In this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system. Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems. The next step is careful pairing with low-cost, low-harm preventive strategies in a pragmatic trial of effectiveness in preventing future suicidality.

Suicidal Behavior in Fibromyalgia Patients: Rates and Determinants of Suicide Ideation, Risk, Suicide, and Suicidal Attempts-A Systematic Review of the Literature and Meta-Analysis of Over 390,000 Fibromyalgia Patients

Background: Suicide is a leading cause of death worldwide, affecting ~800,000 people every year. Fibromyalgia is an extremely prevalent rheumatic disease with a predisposition for comorbid anxiety and depression, which are known risk factors for suicidal behavior. Suicidality and relevant risk factors for suicidal behavior have not been thoroughly studied in patients with fibromyalgia. Objectives: To investigate the risk of suicidal ideation and attempts in patients with fibromyalgia. Methods: A systematic review and meta-analysis was conducted and reported according to the "Preferred Reporting Items for Systematic reviews and Meta-analyses" (PRISMA) standards. Also, the gray literature was extensively searched. Results: Thirteen studies were included in the present systematic review and meta-analysis, including 394,087 fibromyalgia patients. Sample size ranged from 44 to 199,739 subjects, mean age ranged from 45.8 to 54.5 years while the female percentage with fibromyalgia ranged from 17.1 to 100.0%. The overall suicide ideation prevalence was 29.57% (95%CI 1.84-72.07), with an OR 9.12 of (95%CI 1.42-58.77), ranging from 2.34 (95%CI 1.49-3.66) to 26.89 (95%CI 5.72-126.42). Pooled suicide attempt prevalence was 5.69% [95%CI 1.26-31.34], with an OR of 3.12 [95%CI 1.37-7.12]. Suicide risk was higher with respect to the general population with an OR of 36.77 (95%CI 15.55-96.94), as well as suicide events with an HR of 1.38 (95%CI 1.17-1.71). Determinants of suicidality were found to be: employment status, disease severity, obesity and drug dependence, chronic pain and co-morbidities, in particular depression, anxiety, poor sleep, and global mental health. However, in some cases, after adjusting for psychiatric conditions, the threshold of statistical significance was not achieved. Conclusion: Fibromyalgia patients are particularly prone to suicide, in terms of ideation, attempt, risk and events, warranting a pre-emptive screening of their mental health status. Given the few studies available, the high amount of heterogeneity, the evidence of publications bias and the lack of statistical significance when adjusting for underlying psychiatric co-morbidities, further high-quality studies should be conducted. Clinical Trial Registration: ClinicalTrial.gov, identifier 10.17605/OSF.IO/Y4BUE.

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.

The Differences Between Individuals Engaging in Nonsuicidal Self-Injury and Suicide Attempt Are Complex (vs. Complicated or Simple)

BACKGROUND

Why do some people engage in nonsuicidal self-injury (NSSI) while others attempt suicide? One way to advance knowledge about this question is to shed light on the differences between people who engage in NSSI and people who attempt suicide. These groups could differ in three broad ways. First, these two groups may differ in a simple way, such that one or a small set of factors is both necessary and sufficient to accurately distinguish the two groups. Second, they might differ in a complicated way, meaning that a specific set of a large number of factors is both necessary and sufficient to accurately classify them. Third, they might differ in a complex way, with no necessary factor combinations and potentially no sufficient factor combinations. In this scenario, at the group level, complicated algorithms would either be insufficient (i.e., no complicated algorithm produces good accuracy) or unnecessary (i.e., many complicated algorithms produce good accuracy) to distinguish between groups. This study directly tested these three possibilities in a sample of people with a history of NSSI and/or suicide attempt.

METHOD

A total of 954 participants who have either engaged in NSSI and/or suicide attempt in their lifetime were recruited from online forums. Participants completed a series of measures on factors commonly associated with NSSI and suicide attempt. To test for simple differences, univariate logistic regressions were conducted. One theoretically informed multiple logistic regression model with suicidal desire, capability for suicide, and their interaction term was considered as well. To examine complicated and complex differences, multiple logistic regression and machine learning analyses were conducted.

RESULTS

No simple algorithm (i.e., single factor or small set of factors) accurately distinguished between groups. Complicated algorithms constructed with cross-validation methods produced fair accuracy; complicated algorithms constructed with bootstrap optimism methods produced good accuracy, but multiple different algorithms with this method produced similar results.

CONCLUSIONS

Findings were consistent with complex differences between people who engage in NSSI and suicide attempts. Specific complicated algorithms were either insufficient (cross-validation results) or unnecessary (bootstrap optimism results) to distinguish between these groups with high accuracy.

Discovering the Unclassified Suicide Cases Among Undetermined Drug Overdose Deaths Using Machine Learning Techniques

OBJECTIVE

The Centers for Disease Control and Prevention (CDC) monitor accidental and intentional deaths to answer questions that are critical for the development of effective prevention and resource allocation. CDC's National Violent Death Reporting System (NVDRS) is a major innovation in surveillance linking individual-level data from multiple sources. However, suicide underreporting is common, particularly from drug overdose deaths. This study sought to assess machine learning (ML) techniques in quantifying drug overdose suicide underreporting rates.

METHODS

Clinical, sociodemographic, toxicological, and proximal stressor data on overdose decedents (n = 2,665) were extracted from Utah's NVDRS from 2012 to 2015. The existing well-determined cases were used to train and test our ML models. We assessed and compared multiple machine learning methods including Logistic Regression, Random Forest Classifier, Support Vector Machines, and Artificial Neural Networks. We applied a majority voting methodology to classify undetermined drug overdose deaths.

RESULTS

Overdose suicide rates were estimated to be underreported by 33% across all years, increasing yearly from 29% in 2012 to 37% in 2015. The overall test accuracies for all models ranged from 92.3% to 94.6%.

CONCLUSIONS

This research identifies a cost-effective, replicable, and expandable ML-based methodology to estimate the true rates of suicide which may be partially masked during the opioid epidemic.

Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence

Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.

Suicidality in Fibromyalgia: A Systematic Review of the Literature

Fibromyalgia (FM), a poorly understood rheumatic condition, is characterized by chronic pain and psychiatric comorbidities, most notably depression and anxiety. Additional symptoms include sleep difficulties, fatigue, and various cognitive impairments. Furthermore, FM is surrounded by social stigma, due to the unclear nature and etiology of this condition. While there is widespread evidence for the emotional and psychological suffering of those with FM, the scope of suicidality, as well as the underlying factors that are associated with suicidal ideation and behavior among this population, are not well understood. The present review, which is the first of its kind, aims to summarize existing data on the prevalence of suicide-related outcomes among FM patients, highlight factors associated with suicidal ideation and behavior in FM, and identify gaps in the literature to better inform research and clinical care. Studies were extracted from the literature that measured suicidal ideation, attempted suicide, and/or completed suicide among FM patients. Results indicated that both suicidal ideation and suicidal behavior were prevalent among individuals suffering from FM. Psychiatric comorbidity, sleep difficulties, and inpatient hospitalization were associated with both suicidal ideation and suicidal behavior. Functional impairment was associated with suicidal ideation in FM. Factors associated with higher levels of suicidal behavior in FM included female gender, unemployment and lower income, medical comorbidity, and drug dependence. While an understanding of currently recognized risk factors is important for improving FM research and clinical care, some clear methodological and conceptual limitations of the reviewed studies were identified. Future work should focus on longitudinal studies, as well as on gaining a better biological and psychological understanding of the underpinnings of FM and suicidality.

Protecting Life While Preserving Liberty: Ethical Recommendations for Suicide Prevention With Artificial Intelligence

In the United States, suicide increased by 24% in the past 20 years, and suicide risk identification at point-of-care remains a cornerstone of the effort to curb this epidemic (1). As risk identification is difficult because of symptom under-reporting, timing, or lack of screening, healthcare systems rely increasingly on risk scoring and now artificial intelligence (AI) to assess risk. AI remains the science of solving problems and accomplishing tasks, through automated or computational means, that normally require human intelligence. This science is decades-old and includes traditional predictive statistics and machine learning. Only in the last few years has it been applied rigorously in suicide risk prediction and prevention. Applying AI in this context raises significant ethical concern, particularly in balancing beneficence and respecting personal autonomy. To navigate the ethical issues raised by suicide risk prediction, we provide recommendations in three areas-communication, consent, and controls-for both providers and researchers (2).