Journal ArticleDOI
Feature selection and classification model construction on type 2 diabetic patients' data
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TLDR
This work supports the use of data mining as an exploratory tool, particularly as the domain is suffering from a data explosion due to enhanced monitoring and the (potential) storage of this data in the electronic health record.About:
This article is published in Artificial Intelligence in Medicine.The article was published on 2007-11-01. It has received 161 citations till now. The article focuses on the topics: Ranking & Population.read more
Citations
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Unobtrusive Sensing and Wearable Devices for Health Informatics
Yali Zheng,Xiaorong Ding,Carmen C. Y. Poon,Benny Lo,Heye Zhang,Xiao-Lin Zhou,Guang-Zhong Yang,Ni Zhao,Yuan-Ting Zhang +8 more
TL;DR: This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobTrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion.
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A machine learning-based framework to identify type 2 diabetes through electronic health records
TL;DR: A semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate and demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.
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Review: Knowledge discovery in medicine: Current issue and future trend
TL;DR: The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners to explore previous works and identify interesting areas for future research.
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Omic and Electronic Health Record Big Data Analytics for Precision Medicine
TL;DR: This work provides two case studies, including identifying disease biomarkers from multi-omic data and incorporating –omic information into EHR, to demonstrate how big data analytics enables precision medicine.
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A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining
TL;DR: It is found that the existing literature mostly examines analytics in clinical and administrative decision-making, and analytics based on website and social media data has been increasing in recent years.
References
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Journal ArticleDOI
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
William C. Knowler,Elizabeth Barrett-Connor,Sarah E. Fowler,Richard F. Hamman,John M. Lachin,Elizabeth A. Walker,David M. Nathan +6 more
TL;DR: In this paper, the authors compared a lifestyle intervention with metformin to prevent or delay the development of Type 2 diabetes in nondiabetic individuals. And they found that the lifestyle intervention was significantly more effective than the medication.
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Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.
K. G. M. M. Alberti,Paul Zimmet +1 more
TL;DR: A WHO Consultation has taken place in parallel with a report by an American Diabetes Association Expert Committee to re‐examine diagnostic criteria and classification of diabetes mellitus and is hoped that the new classification will allow better classification of individuals and lead to fewer therapeutic misjudgements.
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Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes
Steven E. Nissen,Kathy Wolski +1 more
TL;DR: Patients and providers should consider the potential for serious adverse cardiovascular effects of treatment with rosiglitazone for type 2 diabetes mellitus as well as the availability of outcome data for myocardial infarction and death from cardiovascular causes.
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Instance-Based Learning Algorithms
TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.