کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
518046 867550 2008 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Five-way Smoking Status Classification Using Text Hot-Spot Identification and Error-correcting Output Codes
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Five-way Smoking Status Classification Using Text Hot-Spot Identification and Error-correcting Output Codes
چکیده انگلیسی

We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2b2 task organizers, using micro- and macro-averaged F1 as the primary performance metric. Our best performing system achieved a micro-F1 of 0.9000 on the test collection, equivalent to the best performing system submitted to the i2b2 challenge. Hot-spot identification, zero-vector filtering, classifier weighting, and error correcting output coding contributed additively to increased performance, with hot-spot identification having by far the largest positive effect. High performance on automatic identification of patient smoking status from discharge summaries is achievable with the efficient and straightforward machine learning techniques studied here.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the American Medical Informatics Association - Volume 15, Issue 1, January–February 2008, Pages 32–35
نویسندگان
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