A Systematic Review of Natural Language Processing in Healthcare

Abstract

Systematic reviews and meta-analyses to identify existing clinical natural language processing (NLP) systems that create structured information from unstructured free text have chosen a systematic strategy for reporting items. The study gathers data on the natural language processing methodologies, strategies, procedures, frameworks, and reviews utilized in healthcare applications. We used standard indices like Google Scholar, Scopus, and Web of Sciences to look for articles about NLP in healthcare. We looked for conference proceedings and journal papers published between 2005 and 2020. From the accessible sources, articles concentrating on NLP in the healthcare system were chosen. Forty research articles were evaluated based on their focus on successful activities in the research field. Nineteen publications dealt with methodology, three with frameworks, five with techniques, five with processes, and eight with review research papers. The NLP systems discussed in this paper cover a wide range of clinical and research objectives. This study looks for NLP systems that have tried to solve problems like "processing clinical free text and creating structured output." The data gathered from the highlighted studies was analyzed in order to priorities novel methods and difficulties in clinical NLP.