Title: Biomedical NLP-based data classification for sensitivity evaluation of healthcare data using statistical features analysis

Authors: Manoj Dhawan; Lalit Purohit

Addresses: Shri G.S. Institute of Technology and Science, 23 Sir M. Visvesvaraya Marg, Indore, Madhya Pradesh 452003, India ' Shri G.S. Institute of Technology and Science, 23 Sir M. Visvesvaraya Marg, Indore, Madhya Pradesh 452003, India

Abstract: This work aims to improve the performance of multiclass categorisation of biological texts for sensitivity evaluation by combining two distinct feature representation techniques. Bio ALBERT (a domain-specific adaptation of a lite bidirectional encoder representations from transformers) was utilised in this investigation for designing a multiclass classification model for sensitivity. This study's primary contributions include a weighted feature representation technique for biomedical text categorisation. The research is primarily concerned with combining two different feature representation techniques, namely WE and BoW, to improve the performance of a biomedical multiclass text classification system. The experimental results validate the suggested system's theoretical analysis. This work evaluates the effectiveness and efficiency of the proposed task using the MIMIC-III database. Further, MIMIC III and the PubMed dataset are employed to construct the language model. The performance of the proposed weighted feature representation approach for multiclass classification is found to be superior to the conventional techniques.

Keywords: natural language processing; NLP; transfer learning; attention mechanisms; biomedical NLP; ALBERT; BioALBERT; electronic health records; EHR.

DOI: 10.1504/IJIEI.2026.151802

International Journal of Intelligent Engineering Informatics, 2026 Vol.14 No.1, pp.109 - 128

Received: 10 Jun 2024
Accepted: 01 Oct 2024
Published online: 20 Feb 2026 *

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