A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Pestian, John; Sorter, Michael T.; Connolly, B.; Cohen, K. Bretonnel; McCullumsmith, Cheryl B.; Gee, Jeffry T.; Morency, Louis–Philippe; Scherer, Stefan; Rohlfs, Lesley

doi:10.1111/sltb.12312

Cited by 119 publications

(126 citation statements)

References 49 publications

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“…machine learning), as some researchers have already begun to do (e.g. Kessler et al., ; Pestian et al., ; Walsh, Ribeiro, & Franklin, ). Machine learning may be particularly helpful with meaningfully integrating the many small to modest effects from risk factors and correlates observed in the field (Franklin et al., ).…”

Section: Treatment Of Suicidal Behaviormentioning

confidence: 99%

Annual Research Review: Suicide among youth – epidemiology, (potential) etiology, and treatment

Christine

Franz

Guzmán

et al. 2017

Child Psychology Psychiatry

419

345

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There is much work to be done to better understand suicidal thoughts and behaviors among youth. We strongly encourage future research to: (1) continue improving the conceptualization and operationalization of suicidal thoughts and behaviors; (2) improve etiological understanding by focusing on individual (preferably malleable) mechanisms; (3) improve etiological understanding also by integrating findings across multiple units of analyses and developing short-term prediction models; (4) demonstrate greater developmental sensitivity overall; and (5) account for diverse high-risk populations via sampling and reporting of sample characteristics. These serve as initial steps to improve the scientific approach, knowledge base, and ultimately prevention of suicidal thoughts and behaviors among youth.

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Section: Treatment Of Suicidal Behaviormentioning

confidence: 99%

Annual Research Review: Suicide among youth – epidemiology, (potential) etiology, and treatment

Christine

Franz

Guzmán

et al. 2017

Child Psychology Psychiatry

419

345

View full text Add to dashboard Cite

show abstract

“…Machine learning methodologies are increasingly used in psychiatric research as they facilitate individual-level prediction of unseen observations, which makes them suitable for the development of clinically useful digital tools [46]. Recent evidence has demonstrated the use of these algorithms to utilize clinical and demographic variables to predict suicide attempters among a group of mood disorder patients with accuracy comparable with most breast cancer prediction algorithms [47,48], whereas another study demonstrated the utility of such algorithms to differentiate between suicidal and nonsuicidal patients [49]. Employing these approaches could improve the personalization of care beyond simple cut-off scores and include key risk factors specific to a particular individual.…”

Section: Discussionmentioning

confidence: 99%

Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study

Iorfino¹,

Davenport²,

Ospina-Pinillos³

et al. 2017

J Med Internet Res

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BackgroundSuicidal thoughts are common among young people presenting to face-to-face and online mental health services. The early detection and rapid response to these suicidal thoughts and other suicidal behaviors is a priority for suicide prevention and early intervention efforts internationally. Establishing how best to use new and emerging technologies to facilitate person-centered systematic assessment and early intervention for suicidality is crucial to these efforts.ObjectiveThe aim of this study was to examine the use of a suicidality escalation protocol to respond to suicidality among help-seeking young people.MethodsA total of 232 young people in the age range of 16-25 years were recruited from either a primary mental health care service or online in the community. Each young person used the Synergy Online System and completed an initial clinical assessment online before their face-to-face or online clinical appointment. A suicidality escalation protocol was used to identify and respond to current and previous suicidal thoughts and behaviors.ResultsA total of 153 young people (66%, 153/232) reported some degree of suicidality and were provided with a real-time alert online. Further levels of escalation (email or phone contact and clinical review) were initiated for the 35 young people (15%, 35/232) reporting high suicidality. Higher levels of psychological distress (P<.001) and a current alcohol or substance use problem (P=.02) predicted any level of suicidality compared with no suicidality. Furthermore, predictors of high suicidality compared with low suicidality were higher levels of psychological distress (P=.01), psychosis-like symptoms in the last 12 months (P=.01), a previous mental health problem (P=.01), and a history of suicide planning or attempts (P=.001).ConclusionsThis study demonstrates the use of new and emerging technologies to facilitate the systematic assessment and detection of help-seeking young people presenting with suicidality. This protocol empowered the young person by suggesting pathways to care that were based on their current needs. The protocol also enabled an appropriate and timely response from service providers for young people reporting high suicidality that was associated with additional comorbid issues, including psychosis-like symptoms, and a history of suicide plans and attempts.

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“…A Support Vector Machine (SVM) model was used for classification . SVMs have been proven useful for classification problems such as these, due to their robustness to overfitting and ability to perform well in high‐dimensional spaces . Linear and radial kernels were all considered in training the classifiers, and the kernel with the best overall performance was chosen.…”

Section: Methodsmentioning

confidence: 99%

Identifying epilepsy psychiatric comorbidities with machine learning

et al. 2020

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Objective People with epilepsy are at increased risk for mental health comorbidities. Machine‐learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine‐learning classifiers that identify current or lifetime history of comorbid psychiatric conditions in teenagers and young adults with epilepsy. Materials and Methods Eligible participants were >12 years old with epilepsy. All participants were interviewed using the Mini International Neuropsychiatric Interview (MINI) or the MINI Kid Tracking and asked five open‐ended conversational questions. N‐grams and Linguistic Inquiry and Word Count (LIWC) word categories were used to construct machine learning classification models from language harvested from interviews. Data were analyzed for four individual MINI identified disorders and for three mutually exclusive groups: participants with no psychiatric disorders, participants with non‐suicidal psychiatric disorders, and participants with any degree of suicidality. Performance was measured using areas under the receiver operating characteristic curve (AROCs). Results Classifiers were constructed from 227 interviews with 122 participants (7.5 ± 3.1 minutes and 454 ± 299 words). AROCs for models differentiating the non‐overlapping groups and individual disorders ranged 57%‐78% (many with P < .02). Discussion and Conclusion Machine‐learning classifiers of spoken language can reliably identify current or lifetime history of suicidality and depression in people with epilepsy. Data suggest identification of anxiety and bipolar disorders may be achieved with larger data sets. Machine‐learning analysis of spoken language can be promising as a useful screening alternative when traditional approaches are unwieldy (eg, telephone calls, primary care offices, school health clinics).

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A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Cited by 119 publications

References 49 publications

Annual Research Review: Suicide among youth – epidemiology, (potential) etiology, and treatment

Annual Research Review: Suicide among youth – epidemiology, (potential) etiology, and treatment

Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study

Identifying epilepsy psychiatric comorbidities with machine learning

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