A Natural Language Processing Framework for Analyzing Unstructured Gynecological Health Records

Abstract

The rapid expansion of healthcare data, particularly in electronic health records (EHRs), has created a demand for advanced techniques to extract, process, and analyze clinical information effectively. This paper presents a comprehensive Natural Language Processing (NLP)-based framework tailored to handle unstructured textual data extracted from gynecological patient records. The methodology focuses on segmentation, tokenization, case folding, abbreviation expansion, stemming, and dimensionality reduction to preprocess and normalize data efficiently. Advanced techniques such as negation detection and frequency analysis were implemented to identify patterns and relationships within the data. The proposed framework was validated on a dataset comprising 18,341 gynecological anamnesis records. The analysis included identifying ICD codes, frequent trigrams, and affirmative/negative expressions to assess the patterns and characteristics present in the records. The performance evaluation demonstrated high accuracy (94.15%), precision (92.87%), recall (91.34%), and F1-score (92.10%), indicating the robustness of the approach. The results emphasize the framework's capability to extract key terms and insights, providing valuable support for clinical decision-making and research. This work highlights the potential of NLP methodologies in transforming unstructured clinical data into structured formats, enabling better management of health information and enriching biomedical ontologies for broader applications in healthcare informatics.