Clinical Text Mining for Entity Extraction Using Classical Machine Learning Approaches
Keywords:
Medical Entity Recognition, Clinical Text Mining, Machine Learning TF-IDF Vectorization, Logistic Regression, Natural Language Processing (NLP), Medical Text Classification, Healthcare Informatics, Electronic Health Records (EHR), Named Entity RecognitionAbstract
In the era of digital healthcare, vast volumes of unstructured medical text such as prescriptions, clinical notes, and diagnostic reports are generated daily. Extracting meaningful and structured information from this data is essential for building intelligent healthcare applications, including clinical decision support systems and automated diagnosis tools. This project presents a machine learning-based approach for Medical Entity Recognition (MER), intended to recognize and categorize key entities within medical text into categories such as medications, diseases, procedures, dosages, and administration routes. The system transforms textual data into numerical features using TF-IDF vectorization and employs a Logistic Regression classifier to perform entity classification. A sample dataset is used to instruct and assess the model, with results indicating the feasibility of accurate entity extraction using traditional machine learning methods. In this work, a prototype system has been designed with scalability in mind, allowing future integration of larger datasets and more advanced deep learning models such as BERT or BioBERT. The proposed approach demonstrates significant potential to improve the accuracy and efficiency of medical text analysis, ultimately supporting better clinical decision-making and enhancing patient care outcomes.
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