Applications of Machine Learning and Deep Learning for maintaining Electronic Health Records

Abstract

Due to the advances in machine learning and deep learning models, their use in medical decision frameworks is still minimal. However, the rapid digitization of health information has created an excellent forum for evaluating the effectiveness of such strategies in healthcare. H s a result, an increasing number of research studies are appearing in the fields that use deep learning on Electronic health Records (EHR) for customized threat and health trajectory prediction. Predictive analytics in machine learning is primarily used in the healthcare sector for disease diagnosis. Machine learning approaches aid in the prediction of relationships in EHR results. The health system recommendations in the corpus could be one of the sources, and different healthcare criteria could be element sized, resulting in a structure that has a meaningful effect on healthcare decision-making. Although this is a consistent growth, the field's potential to analyse and differentiate such frameworks for a given scenario has been hindered by considerable paper-to-paper heterogeneity (from sources of data and frameworks used to the research situation they seek to respond).As a result, the objective of this study is to include Machine Learning and Deep Learning Applications for preserving electronic health records. We also hope to: (1) develop and utilize one of the globe's greatest and more important associated primary healthcare EHR datasets as an unique resource for educating such information models; (2) include a guidelines for dealing with EHR data for machine learning; (3) provide a few of the quality standards for evaluating the "awesomeness" of deep learning models in improved clinical forecasting; and (4) suggest future studies for making deep learning models more suitable for EHR data. Our findings identify the importance of interacting with extremely imbalanced records and suggest that concurrent deep learning frameworks like RNN might be better suited to dealing with EHR's spatial design.