Comparative effectiveness research using electronic health records: impacts of oral antidiabetic drugs on the development of chronic kidney disease

2013 ◽  
Vol 22 (4) ◽  
pp. 413-422 ◽  
Author(s):  
Andrew L. Masica ◽  
Edward Ewen ◽  
Yahya A. Daoud ◽  
Dunlei Cheng ◽  
Nora Franceschini ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Electronic Health Records ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Antidiabetic Drugs ◽  
Effectiveness Research ◽  
Oral Antidiabetic Drugs ◽  
Health Records ◽  
Oral Antidiabetic

Importance of Health Information Technology, Electronic Health Records, and Continuously Aggregating Data to Comparative Effectiveness Research and Learning Health Care

2012 ◽  
Vol 30 (34) ◽  
pp. 4243-4248 ◽  
Author(s):  
Benjamin J. Miriovsky ◽  
Lawrence N. Shulman ◽  
Amy P. Abernethy
Keyword(s):  
Electronic Health Records ◽  
Real Time ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Clinical Care ◽  
Administrative Databases ◽  
Effectiveness Research ◽  
Health Records ◽  
Patient Level ◽  
Electronic Health

Rapidly accumulating clinical information can support cancer care and discovery. Future success depends on information management, access, use, and reuse. Electronic health records (EHRs) are highlighted as a critical component of evidence development and implementation, but to fully harness the potential of EHRs, they need to be more than electronic renderings of the traditional paper medical chart. Clinical informatics and structured accessible secure data captured through EHR systems provide mechanisms through which EHRs can facilitate comparative effectiveness research (CER). Use of large linked administrative databases to answer comparative questions is an early version of informatics-enabled CER familiar to oncologists. An updated version of informatics-enabled CER relies on EHR-derived structured data linked with supplemental information to provide patient-level information that can be aggregated and analyzed to support hypothesis generation, comparative assessment, and personalized care. As implementation of EHRs continues to expand, electronic databases containing information collected via EHRs will continuously aggregate; aggregating data enhanced with real-time analytics can provide point-of-care evidence to oncologists, tailored to patient-level characteristics. The system learns when clinical care informs research, and insights derived from research are reinvested in care. Challenges must be overcome, including interoperability, standardization, access, and development of real-time analytics.

Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)

2021 ◽  
pp. 213-233
Author(s):  
Laxmi Kumari Pathak ◽  
Pooja Jha
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Electronic Health Records ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Health Records ◽  
Chronic Kidney Disease Risk ◽  
Electronic Health ◽  
Heart Disorders

Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.

scholarly journals Comparative effectiveness of incident oral antidiabetic drugs on kidney function

2012 ◽  
Vol 81 (7) ◽  
pp. 698-706 ◽  
Author(s):  
Adriana M. Hung ◽  
Christianne L. Roumie ◽  
Robert A. Greevy ◽  
Xulei Liu ◽  
Carlos G. Grijalva ◽  
...  
Keyword(s):  
Kidney Function ◽  
Comparative Effectiveness ◽  
Antidiabetic Drugs ◽  
Oral Antidiabetic Drugs ◽  
Oral Antidiabetic
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