Historically, suicide risk assessment has relied on question-and-answer type tools. These tools, built on psychometric advances, are widely used because of availability. Yet there is no known tool based on biologic and cognitive evidence. This absence often cause a vexing clinical problem for clinicians who question the value of the result as time passes. The purpose of this paper is to describe one experiment in a series of experiments to develop a tool that combines Biological Markers (Bm) with ThoughtMarkers (Tm), and use machine learning to compute a real-time index for assessing the likelihood repeated suicide attempt in the next six-months. For this study we focus using unsupervised machine learning to distinguish between actual suicide notes and newsgroups. This is important because it gives us insight into how well these methods discriminate between real notes and general conversation.
CITATION STYLE
Matykiewicz, P., Duch, W., & Pestian, J. (2009). Clustering semantic spaces of suicide notes and newsgroups articles. In BioNLP 2009 - Biomedical Natural Language Processing Workshop, BioNLP 2009 - held in conjunction with 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2009 - Proceedings (pp. 179–184). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1572364.1572389
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