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Advancement of natural language programming, machine learning and electronic health records for the digital health science field

The widespread use of “electronic health record systems (EHRs)” in health care provides a large amount of real-world data, opening up new opportunities for medical trials. “Deep learning, a subset of machine learning (ML)”, has experienced a meteoric rise in popularity over the last six years, owing to advances in computing power and the accessibility of enormous new datasets. “Natural language processing (NLP)” approaches have been used as an “artificial intelligence strategy” to obtain information from medical narratives in EHRs, as a huge quantity of useful clinical knowledge is contained in clinical stories. This NLP capacity may enable “automated chart review in clinical care to identify individuals with distinct clinical features” and decrease methodological heterogeneity in establishing “phenotypes, masking biological heterogeneity in allergy, asthma, and immunology research”. Aim of this research paper is to understand the advanced technologies such as “Machine Learning, Natural Language Programming” are helpful for digital health field. In this context, secondary data collection method is used to gather information related to this topic.