Biomedical NLP Based Adaptive Security Framework for Healthcare Data

This paper presents an adaptive security framework for biomedical data which offers the security on the basis of the data sensitivity and relevance. This research aims to improve the efficiency of multiclass classification of biological texts for sensitivity evaluation by combining two distinct feature representation methodologies. Multiple statistical weighting techniques, including as class probability (CP), inverse document frequency (IDF), and term frequency (TF), were considered for use with each component of the WE vectors in an effort to unify the two feature representations. BioALBERT, a variant of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), was used in this work. Using the feature vector combinations that were taken into consideration, we aimed to classify every possible activity when developing this BioALBERT-based multiclass classification model. The experimental testing validates the conclusions of the theoretical study of the proposed system. The utility and feasibility of the task were evaluated in this study using the MIMIC-III database.