Analysis of Electronic Health Records Based on Deep Learning with Natural Language Processing

The interdisciplinary research field concentrated on interfaces between human languages and computers is natural language processing (NLP). Recent developments to solve NLP problems have been followed by deep learning. Deep learning implementations in the health care industry are mostly related to traditional examples of the technology of medical tests for detecting diseases through image processing or computer viewing techniques. Another source of information that is often ignored, if not more important than medical scanning, is the electronic health record (EHR), which can change the way to access valuable features and data from patients' medical records. The electronic health record (EHR) model's comprehensive adoption allows large-scale collection of health data from real clinical settings. In this paper, the Adaptive Hybridized Deep Neural Network has been proposed for electronic health records. Deep Neural Network has been utilized for the effective clinical record system. EHRs have several classifications, and controlled vocabularies record the appropriate medical information and events. Various EHR deep learning systems that easily share functional analyses and implementations introduced multiple clinical code types. EHR documents are mainly used to store patient data, such as patient medical history, development, age, Diagnosis, and treatment. Our findings illustrate the complexity of using highly imbalanced data sets and demonstrate that consecutive, deep learning architectures such as DNN may be better suited to tackle EHR's temporal structure.

Automated summary

This paper presents an adaptive hybridized deep neural network for electronic health records and discusses the importance and applications of electronic health records in the healthcare industry. The research findings demonstrate that deep learning architectures can better handle the temporal structure of electronic health records.