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Articles 1 - 3 of 3
Full-Text Articles in Medicine and Health Sciences
Secondary Use Of Structured Electronic Health Records Data: From Observational Studies To Deep Learning-Based Predictive Modeling, Laila Bekhet
Dissertations & Theses (Open Access)
With the wide adoption of electronic health records (EHRs), researchers, as well as large healthcare organizations, governmental institutions, insurance, and pharmaceutical companies have been interested in leveraging this rich clinical data source to extract clinical evidence and develop predictive algorithms. Large vendors have been able to compile structured EHR data from sites all over the United States, de-identify these data, and make them available to data science researchers in a more usable format. For this dissertation, we leveraged one of the earliest and largest secondary EHR data sources and conducted three studies of increasing scope. In the first study, which …
Enhance Representation Learning Of Clinical Narrative With Neural Networks For Clinical Predictive Modeling, Yuqi Si
Dissertations & Theses (Open Access)
Medicine is undergoing a technological revolution. Understanding human health from clinical data has major challenges from technical and practical perspectives, thus prompting methods that understand large, complex, and noisy data. These methods are particularly necessary for natural language data from clinical narratives/notes, which contain some of the richest information on a patient. Meanwhile, deep neural networks have achieved superior performance in a wide variety of natural language processing (NLP) tasks because of their capacity to encode meaningful but abstract representations and learn the entire task end-to-end. In this thesis, I investigate representation learning of clinical narratives with deep neural networks …
Standardizing New Diagnostic Tests To Facilitate Rapid Responses To The Covid-19 Pandemic, Xiao Dong
Standardizing New Diagnostic Tests To Facilitate Rapid Responses To The Covid-19 Pandemic, Xiao Dong
Dissertations & Theses (Open Access)
In order to enhance the data interoperability, an expeditious and accurate standardization solution is highly desirable for naming rapidly emerging novel lab tests, and thus diminishes confusion in early responses to pandemic outbreaks. This is a preliminary study to explore the roles and implementation of medical informatics technology, especially natural language processing and ontology methods, in standardizing information about emerging lab tests during a pandemic, thereby facilitating rapid responses to the pandemic. The ultimate goal of this study is to develop an informatics framework for rapid standardization of lab testing names during a pandemic to better prepare for future public …