The use of natural language processing in medical record analysis
Review
Keywords:
Clinical Notes, Electronic Health Records, Chronic Diseases, Natural LanguageAbstract
Background: In order to address the increasing prevalence of chronic illnesses globally, it is necessary to develop innovative methods that supplement and surpass evidence-based treatment in this field. An optimistic approach is the use of electronic health records (EHRs) for the purpose of conducting clinical and translational research by analyzing patient data. Machine learning methods used to electronic health records (EHRs) are leading to enhanced comprehension of patient clinical paths and the ability to forecast the risk of chronic diseases. This presents a distinct chance to uncover previously unidentified clinical knowledge. Nevertheless, a substantial amount of clinical histories are still inaccessible due to being stored as unstructured free-form text. Unlocking the whole potential of EHR data relies on the advancement of natural language processing (NLP) techniques to automatically convert clinical text into structured clinical data. This structured data may then be used to inform clinical choices and perhaps postpone or prevent the start of diseases. Aim of Work: The aim of the study was to provide a thorough examination of the progress and adoption of NLP techniques used in analyzing clinical notes about chronic illnesses. This included exploring the difficulties encountered by NLP methodology in comprehending clinical narratives.
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