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Deep learning for electronic health records

Electronic medical records (EMRs) were primarily introduced as a digital health tool in hospitals to improve patient care, but over the past decade, research works have implemented EMR data in clinical trials and omics studies to increase translational potential in drug development
Deep learning for electronic health records

Electronic health records (EHR) systems store data associated with each individual’s health journey (including demographic information, diagnoses, medications, laboratory tests and results, medical images, clinical notes, and more). While the primary use of EHR was to improve the efficiency and ease of access of health systems , it has found a lot of applications in clinical informatics and epidemiology . In particular, EHR have been used for medical concept extraction, disease and patient clustering, patient trajectory modelling, disease prediction , and data-driven clinical decision support, to name a few.

The early analyses of EHR relied on simpler and more traditional statistical techniques. More recently, however, statistical machine learning techniques such as logistic regression, support vector machines (SVM), Cox proportional hazard model , and random forest have also been employed for mining reliable predictive patterns in EHR data.

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Deep learning for electronic health records: A comparative review of multiple deep neural architectures
Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digita…

#DeepLearning #EHR #AI #ML #Probyto #ProbytoAI

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