RETRACTED ARTICLE: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

Just, Marcel Adam; Pan, Lisa; Cherkassky, Vladimir L.; McMakin, Dana L.; Cha, Christine; Nock, Matthew K.; Brent, David A.

doi:10.1038/s41562-017-0234-y

Cited by 202 publications

(138 citation statements)

References 44 publications

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“…In other words, when it comes to predicting suicide attempts, AI appears to be better than human beings, although the clinical applicability in the real world remains unproven 23. In another study, researchers used machine-learning algorithms to identify individuals at risk of suicide with high (91%) accuracy, based on their altered fMRI neural signatures of death-related and life-related concepts 24. These developments in AI are now being applied.…”

Section: Resultsmentioning

confidence: 99%

Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health

Loh

2018

leader

125

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Artificial intelligence (AI) has the potential to significantly transform the role of the doctor and revolutionise the practice of medicine. This qualitative review paper summarises the past 12 months of health research in AI, across different medical specialties, and discusses the current strengths as well as challenges, relating to this emerging technology. Doctors, especially those in leadership roles, need to be aware of how quickly AI is advancing in health, so that they are ready to lead the change required for its adoption by the health system. Key points: ‘AI has now been shown to be as effective as humans in the diagnosis of various medical conditions, and in some cases, more effective.’ When it comes to predicting suicide attempts, recent research suggest AI is better than human beings. ‘AI’s current strength is in its ability to learn from a large dataset and recognise patterns that can be used to diagnose conditions, putting it in direct competition with medical specialties that are involved in diagnostic tests that involve pattern recognition, such as pathology and radiology’. The current challenges in AI include legal liability and attribution of negligence when errors occur, and the ethical issues relating to patient choices. ‘AI systems can also be developed with, or learn, biases, that will need to be identified and mitigated’. As doctors and health leaders, we need to start preparing the profession to be supported by, partnered with, and, in future, potentially be replaced by, AI and advanced robotics systems.

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Section: Resultsmentioning

confidence: 99%

Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health

Loh

2018

leader

125

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“…The tasks often used in conjunction with fMRI are similar to those used in the psychiatric literature for mood disorders and include measures of response inhibition (Pan et al., ), decision making (Jollant et al., ), and response to emotional faces (Jollant et al., ; Pan et al., ). It may be beneficial to more specifically probe cognitive biases underlying the suicidal state, as recent evaluations have suggested that suicidal individuals have altered neural responses to life and death words (Just et al., ). It is possible that illuminating the neural correlates of how individuals consider the self in relation to life or death can be used to better understand suicidal cognition.…”

mentioning

confidence: 99%

Functional Imaging of the Implicit Association of the Self With Life and Death

Ballard

Reed

Szczepanik

et al. 2019

Suicide & Life Threat Behav

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Objective A critical need exists to identify objective markers of suicide ideation. One potential suicide risk marker is the Suicide Implicit Association Task (S‐IAT), a behavioral task that uses differential reaction times to compare the implicit association between the self and death to the implicit association between the self and life. Individuals with a stronger association between the self and death on the S‐IAT are more likely to attempt suicide in the future. To better understand the neural underpinnings of the implicit association between self and either life or death, a functional magnetic resonance imaging (fMRI) version of the S‐IAT was adapted and piloted in healthy volunteers. Method An fMRI version of the S‐IAT was administered to 28 healthy volunteers (ages 18–65, 14F/14M). Results Behavioral results were comparable to those seen in non‐scanner versions of the task. The task was associated with patterns of neural activation in areas relevant to emotional processing, specifically the insula and right ventrolateral prefrontal cortex. Conclusions Performance on the S‐IAT fMRI task may reflect scores obtained outside of the scanner. In future evaluations, this task could help assess whether individuals at increased risk of suicide display a different pattern of neural activation in response to self/death and self/life stimuli.

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“…In [13], the author made an attempt to understanding the representations of neuron with in death-related and suicide-related concepts. And measures the performance of classifier in determining the state of participant.…”

Section: Resultsmentioning

confidence: 99%