کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10355871 | 867557 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
This paper addresses a very specific problem of identifying patients diagnosed with a specific condition for potential recruitment in a clinical trial or an epidemiological study. We present a simple machine learning method for identifying patients diagnosed with congestive heart failure and other related conditions by automatically classifying clinical notes dictated at Mayo Clinic. This method relies on an automatic classifier trained on comparable amounts of positive and negative samples of clinical notes previously categorized by human experts. The documents are represented as feature vectors, where features are a mix of demographic information as well as single words and concept mappings to MeSH and HICDA classification systems. We compare two simple and efficient classification algorithms (Naïve Bayes and Perceptron) and a baseline term spotting method with respect to their accuracy and recall on positive samples. Depending on the test set, we find that Naïve Bayes yields better recall on positive samples (95 vs. 86%) but worse accuracy than Perceptron (57 vs. 65%). Both algorithms perform better than the baseline with recall on positive samples of 71% and accuracy of 54%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Biomedical Informatics - Volume 38, Issue 2, April 2005, Pages 145-153
Journal: Journal of Biomedical Informatics - Volume 38, Issue 2, April 2005, Pages 145-153
نویسندگان
Serguei V. Pakhomov, James Buntrock, Christopher G. Chute,