Natural Language Processing Approaches for Automated Multilevel and Multiclass Classification of Breast Lesions on Free-Text Cytopathology Reports.

Purpose: The extensive growth and use of electronic health records (EHRs) and extending medical literature have led to huge opportunities to automate the extraction of relevant clinical information that helps in concise and effective clinical decision support. However, processing such information has traditionally been dependent on labor-intensive processes with human errors such as fatigue, oversight, and interobserver variability. Hence, this study aims at the processing of EHRs and performing multilevel and multiclass classification by fetching dominant characteristic features that are sufficient to detect and differentiate various types of breast lesions.