A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial (bibtex)
by John P. Pestian, Michael Sorter, Brian Connolly, Kevin Bretonnel Cohen, Cheryl McCullumsmith, Jeffry T. Gee, Louis-Philippe Morency, Stefan Scherer, Lesley Rohlfs
Abstract:
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.
Reference:
A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial (John P. Pestian, Michael Sorter, Brian Connolly, Kevin Bretonnel Cohen, Cheryl McCullumsmith, Jeffry T. Gee, Louis-Philippe Morency, Stefan Scherer, Lesley Rohlfs), In Suicide and Life-Threatening Behavior, 2016.
Bibtex Entry:
@article{pestian_machine_2016,
	title = {A {Machine} {Learning} {Approach} to {Identifying} the {Thought} {Markers} of {Suicidal} {Subjects}: {A} {Prospective} {Multicenter} {Trial}},
	issn = {03630234},
	url = {http://doi.wiley.com/10.1111/sltb.12312},
	doi = {10.1111/sltb.12312},
	abstract = {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.},
	journal = {Suicide and Life-Threatening Behavior},
	author = {Pestian, John P. and Sorter, Michael and Connolly, Brian and Bretonnel Cohen, Kevin and McCullumsmith, Cheryl and Gee, Jeffry T. and Morency, Louis-Philippe and Scherer, Stefan and Rohlfs, Lesley},
	month = nov,
	year = {2016},
	keywords = {UARC, Virtual Humans}
}
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