کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
504967 864455 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A flexible data-driven comorbidity feature extraction framework
ترجمه فارسی عنوان
یک چارچوب استخراج ویژگی همبودی داده محور انعطاف پذیر
کلمات کلیدی
کشف دانش؛ همراهی؛ خوشه بندی؛ RMS پیش بینی ، سیستم کنترل از راه دور ؛ EHR، پرونده های سلامتی الکترونیکی؛ ICD، طبقه بندی بین المللی بیماری ها؛ ICD-9-CM، ICD نسخه 9، اصلاح بالینی؛ LOS، طول اقامت؛ HR، موش قلب
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• A data-driven approach for feature extraction from disease diagnostic information.
• Classification schemes for categorizing patients into high and low-risk cohorts.
• The new scheme outperforms Charlson's and Elihauser's schemes.

Disease and symptom diagnostic codes are a valuable resource for classifying and predicting patient outcomes. In this paper, we propose a novel methodology for utilizing disease diagnostic information in a predictive machine learning framework. Our methodology relies on a novel, clustering-based feature extraction framework using disease diagnostic information. To reduce the data dimensionality, we identify disease clusters using co-occurrence statistics. We optimize the number of generated clusters in the training set and then utilize these clusters as features to predict patient severity of condition and patient readmission risk. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million hospital discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 Congestive Heart Failure (CHF) patients and the UCI 130-US diabetes dataset that includes admissions from 69,980 diabetic patients. We compare our cluster-based feature set with the commonly used comorbidity frameworks including Charlson's index, Elixhauser's comorbidities and their variations. The proposed approach was shown to have significant gains between 10.7–22.1% in predictive accuracy for CHF severity of condition prediction and 4.65–5.75% in diabetes readmission prediction.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 73, 1 June 2016, Pages 165–172
نویسندگان
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