We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.
PDF (1.19 MB PDF FORMAT)
RIS citation (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)
BibTex citation (BIBDESK, LATEX)
It's a great experience publishing with Biomedical Informatics Insights. I am particularly impressed with the in-depth and constructive comments provided by the reviewers within such a short time-frame. The typesetting was not only prompt, but most importantly, effective. In fact, this was among the very few publication experiences that I have had when no correction was needed in the author proofs. I highly recommend Biomedical Informatics Insights to both readers and prospective ...
All authors are surveyed after their articles are published. Authors are asked to rate their experience in a variety of areas, and their responses help us to monitor our performance. Presented here are their responses in some key areas. No 'poor' or 'very poor' responses were received; these are represented in the 'other' category.See Our Results
Copyright © 2013 Libertas Academica Ltd (except open access articles and accompanying metadata and supplementary files.)