Clinical Analytics of Medical Data of Patients Using Natural Language Processing Approach
Keywords:
Clinical analytics, medical data of patients, min-max normalization, natural language processing (NLP)Abstract
Clinical analytics is essential to the delivery of modern healthcare because it allows healthcare professionals to glean insightful information from massive patient data sets. Natural Language Processing (NLP) methods have become effective tools for healthcare textual data analysis in recent years. However, analysts' language evaluations are extremely individualized and vulnerable to the assessor's prejudices. Furthermore, language contains information that is typically hidden from observers. One potential approach to reducing the constraints of a person's evaluation of language is to supplement clinical evaluation with NLP. In this study, we investigate using NLP approaches to assess textual medical data and support evidence-based healthcare decision-making. There are various steps to the suggested strategy. First, the text is cleaned and standardized using the min-max normalization data preparation approach, which removes noise and extraneous information. Then, we aim to extract pertinent clinical data from various sources, including doctor notes, discharge summaries, and pathology reports, such as diagnoses, treatments, and patient outcomes. According to experimental findings, the proposed technology outperforms conventional techniques in clinical analytics of medical data with high accuracy, precision, recall, f1-score, the area under the curve (AUC), and lowest mean absolute percentage error (MAPE). Furthermore, NLP may be used to create predictive models that forecast patient outcomes or identify high-risk people for focused therapies. Significant implications for medical care and healthcare administration flow from the findings of this study.
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