What's in a Note: Construction of a Suicide Note Corpus

Pestian, John; Matykiewicz, Paweł; Linn-Gust, Michelle

doi:10.4137/bii.s10213

Cited by 36 publications

(37 citation statements)

References 10 publications

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“…Shame is prominent in studies of male suicide attempters 22 . In a content analysis of over 1200 suicide notes, sadness (e.g., hopelessness, sorrow), anger (e.g., anger, blame), and guilt were particularly prominent, although positive emotions that expressed relief either on the part of the suicide victim or the intended recipient of the note were common 23 . However, note that our neurosemantic tests here probe for the emotional content in the representation of particular concepts (such as death ), not for an enduring emotional trait.…”

Section: Resultsmentioning

confidence: 99%

RETRACTED ARTICLE: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

et al. 2017

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The clinical assessment of suicidal risk would be significantly complemented by a biologically-based measure that assesses alterations in the neural representations of concepts related to death and life in people who engage in suicidal ideation. This study used machine-learning algorithms (Gaussian Naïve Bayes) to identify such individuals (17 suicidal ideators vs 17 controls) with high (91%) accuracy, based on their altered fMRI neural signatures of death and life-related concepts. The most discriminating concepts were death, cruelty, trouble, carefree, good, and praise. A similar classification accurately (94%) discriminated 9 suicidal ideators who had made a suicide attempt from 8 who had not. Moreover, a major facet of the concept alterations was the evoked emotion, whose neural signature served as an alternative basis for accurate (85%) group classification. The study establishes a biological, neurocognitive basis for altered concept representations in participants with suicidal ideation, which enables highly accurate group membership classification.

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Section: Resultsmentioning

confidence: 99%

RETRACTED ARTICLE: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

et al. 2017

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“…In an international, shared task‐setting that included multiple groups sharing the same task definition, data set, and scoring metric (Voorhees et al., ), 24 teams developed and tested computational algorithms to identify emotions in over 1,319 suicide notes written shortly before death. The results showed that the fusion of multiple methods outperform single methods (Pestian, Matykiewicz, & Linn‐Gust, ).…”

Section: Introductionmentioning

confidence: 99%

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

Pestian

Sorter

Connolly

et al. 2016

Suicide & Life Threat Behav

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119

118

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Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects' words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.

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“…Depending on time and culture, the percentage of people who die by suicide who leave notes seems to vary substantially. In the United States, the percentage of people who die by suicide and leave a suicide note has been reported to range from approximately 10% and 43% (Pestian, Matykiewicz, & Linn‐Gust, ). Interestingly, it seems that the rate of suicide notes did not increase in Japan despite rising suicide rates, indicating that the reasons for suicide might not be related to the act of writing them (Shioiri et al., ).…”

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confidence: 99%

Who Leaves Suicide Notes? A Six‐Year Population‐Based Study

Cerel

Moore

Brown

et al. 2014

Suicide & Life Threat Behav

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Popular culture insists on the significance of suicide notes, but research has yielded conflicting results about who leaves notes. Utilizing 6 years of suicides from a comprehensive statewide data surveillance system, differences were examined between cases with suicide notes and those without in terms of demographics, circumstances of the suicide, and precipitating circumstances. Of the 2,936 suicides, 18.25% included a note. Demographics and circumstances did not differ for cases with a note compared to cases with no note. Results have implications for working with people bereaved by suicide in helping understand that the notes are uncommon and not systematic. However, it is also possible that for some individuals, the content of a note is meaningful and can help or hinder their course of bereavement.

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What's in a Note: Construction of a Suicide Note Corpus

Cited by 36 publications

References 10 publications

RETRACTED ARTICLE: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

RETRACTED ARTICLE: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

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

Who Leaves Suicide Notes? A Six‐Year Population‐Based Study

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