Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory.
By R. Richesson, W. E. Hammond, Meredith Nahm, D. Wixted, Gregory E. Simon, Jennifer G. Robinson, Alan Bauck, Denise Cifelli, Michelle M. Smerek, J. Dickerson and 5 more. 2013
199 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Health Information Management, Management Science and Operations Research
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In Economics and Econometrics
In Health Information Management
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Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “, height, weight, BMI), biochemical processes, or behaviors [1].”
- Clinical Text Data in Machine Learning: Systematic Review (2020, background): “EHR-based phenotyping approaches leverage data collected routinely in the course of health care delivery to identify cohorts of individuals that share certain clinical characteristics, events, and service patterns [136].”
- Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “cation of ML in healthcare [206], which is known as phenotyping [207].”
- A Review of Challenges and Opportunities in Machine Learning for Health. (2018, methodology): “Phenotyping is an important goal in healthcare 30 , and wearable data provides an ongoing way for devices to collect continuous non-invasive data and provide meaningful classifications or alerts when a patient needs attention.”
- Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, methodology): “The process of mapping raw EHR data into meaningful medical concepts, or the task of learning the medically relevant characteristics of the data [24,25] is referred to as EHR-based phenotyping.”