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Open Access

ARTICLE

An NLP-Based Neuro-Semantic Clinical Filter for Medical Text Simplification

Akmalbek Abdusalomov1, Kudratjon Zohirov2, Azizbek Khojamurotov3, Furkat Safarov3,4, Alpamis Kutlimuratov5, Jasur Sevinov6,7, Zavqiddin Temirov8, Abror Buriboev5,9,10, Heung Seok Jeon11,*
1 Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si, Republic of Korea
2 Department of Software and Technical Support of Computer Systems, Karshi State Technical University, Karshi, Uzbekistan
3 Department of Computer Systems, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent, Uzbekistan
4 Department of Information Systems and Technologies, Tashkent State University of Economics, Tashkent, Uzbekistan
5 Department of Applied Informatics, Kimyo International University in Tashkent, Tashkent, Uzbekistan
6 Department of Information Processing and Control Systems, Tashkent State Technical University, Tashkent, Uzbekistan
7 Department of Computer Engineering, University of Tashkent for Applied Sciences, Tashkent, Uzbekistan
8 Department of Digital Technologies, Alfraganus University, Tashkent, Uzbekistan
9 Department of Software Engineering, Samarkand State University, Samarkand, Uzbekistan
10 Department of IT, Samarkand Branch of Tashkent University of Information Technologies, Uzbekistan
11 Department of Computer Engineering, Konkuk University, Chungju, Republic of Korea
* Corresponding Author: Heung Seok Jeon. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.079237

Received 17 January 2026; Accepted 03 May 2026; Published online 08 June 2026

Abstract

Medical texts are often complex and difficult to understand for non-specialists, creating barriers to effective communication in the clinical and rehabilitation fields. Although recent advances in natural language processing (NLP) have enabled automated text simplification, existing approaches often struggle to maintain medical accuracy and frequently result in factual inconsistencies or distortions. To address these issues, we propose the Neuro-Semantic Clinical Filter (NSCF), a novel NLP-based framework designed for clinically accurate simplification of medical texts. The proposed method integrates a Medical Concept Graph Encoder (MCGE) to incorporate structured domain knowledge, a Neuro-Symbolic Transformer (NSTR) for supervised text generation, and a Knowledge Integrity Validator (KIV) to ensure factual consistency during decoding. Furthermore, an adaptive module (ClinAdapt) enables text personalization based on patient profiles and comprehension levels. Extensive experiments were conducted on several medical text corpora, comparing NSCF with modern baseline models, including BART, T5, and domain-specific variants. Experimental results show that the NSCF demonstrates improved performance across several metrics, including a SARI score of 47.2, a BERTS score of 91.6, and a factual alignment ratio (FAR) of 88.1. Human evaluation further confirms improvements in reading fluency, accuracy, and clinical applicability. These results highlight the effectiveness of the proposed approach in bridging the gap between complex medical language and patient-level understanding, providing a robust and interpretable solution for real-world healthcare applications.

Keywords

Patient health literacy; factuality preservation; biomedical knowledge graphs; controlled language generation; explainable clinical NLP

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