Machine learning discovery of longitudinal patterns of depression and suicidal ideation

Gong, Jue; Simon, Gregory E.; Liu, Shan

doi:10.1371/journal.pone.0222665

Cited by 23 publications

(16 citation statements)

References 32 publications

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“…( 28 ) may in the future contain an order of magnitude richer source of data for monitoring treatment quality, outcome and suicide research. In particular, availability of repeated symptom scores plus trait measures concurrently with updated information on family situation and employment will result in more accurate view of factors influencing suicide risk ( 46 – 48 ). More sophisticated machine learning tools may be helpful in uncovering previously unobserved patterns related to risk, but statistical rarity of suicide will unavoidably limit predictive ability also in the future, irrespective of sophistication of analytical tools ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

Suicides in Mood Disorders in Psychiatric Settings in Nordic National Register–Based Studies

Isometsä

2020

Front. Psychiatry

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Objective: Although risk factors for nonfatal suicidal behavior in mood disorders have been vastly investigated, rate and risk factors of suicide deaths are less well known. Extensive health care and other population registers in the Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) allow national-level studies of suicide rates and risk factors. This systematic review examined Nordic studies of suicide in mood disorders. Methods: National Nordic studies published after 1.1.2000 reporting on suicide mortality or relative risk in diagnosed unipolar depression or bipolar disorder treated in psychiatric settings; temporal variations in suicide risk after discharge, or risk factors for suicide were systematically reviewed. Results: Altogether 16 longitudinal studies reported on rate and risk of suicide in depression. They found 2%-8% of psychiatric inpatients with depression to have died by suicide. However, in Finland suicide risk among depressive inpatients halved since the early 1990s. Nine studies investigated suicide risk in bipolar disorder, finding 4-8% of patients having died by suicide in long term. The relative risk of suicide was consistently found extremely high (SMR > 100) during the first weeks postdischarge, declining steeply over time to approximately SMR of five after five years. Male gender, preceding suicide attempts, high severity of depression and substance abuse were found risk factors for suicide in depression, with only minor gender differences in risk factors, but major differences in lethal methods. Three studies investigated risk factors for suicide in bipolar disorder, finding male gender, preceding suicide attempts, and depressive episodes and psychiatric comorbidity to be associated with risk. Conclusions: Overall, of psychiatric inpatients with depressive of bipolar disorders in the Nordic countries, 2%-8% have died by suicide in the last few decades, but current rates may be lower. Suicide risk is approximately similar or somewhat higher among patients with bipolar disorder, risk factor studies of whom are fewer. Risk of suicide is remarkably high immediately after discharge, and higher among males than females, those with preceding suicide attempts, high severity of depression, or concurrent substance abuse. Generalizability of findings from these Nordic studies to other countries need to be investigated, and their methodological limitations understood.

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Section: Discussionmentioning

confidence: 99%

Suicides in Mood Disorders in Psychiatric Settings in Nordic National Register–Based Studies

Isometsä

2020

Front. Psychiatry

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“…14 These machine learning methods have shown better performance in predicting unmeasured outcomes as compared to conventional statistical methods. 15,16 For suicidal risks in association with psychological variables, previous history of suicidal attempts, 17 current suicidal ideation, 18 upcoming suicidal attempts 19 as well as the subtypes of longitudinal trajectories in changes of depressive symptoms and suicidal ideation 20 have been classified by way of the machine learning methods that applied several psychological symptoms including depressive mood, family-and pharmacotherapy-related features as explanatory features. On the other hand, few previous machine learning method-based big data studies have explored the risk factors of suicidality from the non-psychological variables to be prioritized in the primary care clinic.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study

Yun

Yeo

et al. 2020

Psychiatry Investig

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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.

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“…The time-variant changes in these factors make the task of diagnosis for MHPs more challenging [ 15 ]. Even though Electronic Health Records (EHRs) are longitudinal, studies have predominantly relied on time-invariant modeling of content to predict suicide-related ideations, suicide-related behaviors, and suicide attempt [ 16 , 17 ]. This approach is often employed due to patients’ low engagement and poor treatment adherence resulting ill-informed follow-up diagnostic procedure.…”

Section: Introductionmentioning

confidence: 99%

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

et al. 2021

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Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential—most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.

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Machine learning discovery of longitudinal patterns of depression and suicidal ideation

Cited by 23 publications

References 32 publications

Suicides in Mood Disorders in Psychiatric Settings in Nordic National Register–Based Studies

Suicides in Mood Disorders in Psychiatric Settings in Nordic National Register–Based Studies

Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

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