Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records
نویسندگان
چکیده
University at Buffalo, University of Colorado, Denver, Icahn School of Medicine at Mount Sina
منابع مشابه
Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques
Background: Millions of deaths occur around the world each year due to lack of access to appropriate treatment for chronic kidney disease patients. Given the importance and mortality rate of this disease, early and low-cost prediction is very important. The researchers intend to identify chronic kidney disease through the optimal combination of techniques used in different stages of data mining...
متن کاملRisk Prediction with Electronic Health Records: A Deep Learning Approach
The recent years have witnessed a surge of interests in data analytics with patient Electronic Health Records (EHR). Data-driven healthcare, which aims at effective utilization of big medical data, representing the collective learning in treating hundreds of millions of patients, to provide the best and most personalized care, is believed to be one of the most promising directions for transform...
متن کاملDevelopment and validation of a classification approach for extracting severity automatically from electronic health records
BACKGROUND Electronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity ...
متن کاملMethodological challenges when carrying out research on CKD and AKI using routine electronic health records.
Research regarding chronic kidney disease (CKD) and acute kidney injury (AKI) using routinely collected data presents particular challenges. The availability, consistency, and quality of renal data in electronic health records has changed over time with developments in policy, practice incentives, clinical knowledge, and associated guideline changes. Epidemiologic research may be affected by pa...
متن کاملDevelopment and validation of an electronic phenotyping algorithm for chronic kidney disease
Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. A...
متن کامل