Combining deep learning with token selection for patient phenotyping from electronic health records
نویسندگان
چکیده
منابع مشابه
Deep Representation for Patient Visits from Electronic Health Records
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models anticipated to drive personalized medicine and improve healthcare quality. T...
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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...
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OBJECTIVE To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications. MATERIALS AND METHODS We conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimer's disease, breast cancer, gout, ...
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The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new ...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2020
ISSN: 2045-2322
DOI: 10.1038/s41598-020-58178-1