Advancing Clinical Data Management with AI Techniques: A TVET Approach for Capacity Building in Nigerian Polytechnics

Bright Ezeoha; Etim; Emmanuel Okon; Ezichi Onyerionwu

doi:10.56201/ijcsmt.vol.11.no12.2025.pg117.134

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Advancing Clinical Data Management with AI Techniques: A TVET Approach for Capacity Building in Nigerian Polytechnics

Bright Ezeoha, Etim, Emmanuel Okon, Ezichi Onyerionwu

Abstract

This research project aims at innovating and testing deep learning and natural language processing techniques for clinical data management. Amidst the burgeoning influx of clinical data and the inherent inefficiencies of manual review processes in healthcare, this study proposes a pioneering approach leveraging advanced Deep Learning (DL) and Natural Language Processing methodologies. The primary aim is to devise a system that automates the extraction and analysis of medical encounter data, derived from voice recordings, into structured textual formats. The system integrates cutting-edge DL models for precise speech recognition and employs sophisticated NLP algorithms to comprehend and extract pertinent medical information from textual data. Object-Oriented Analysis and Design Methodology governs the architectural framework, ensuring scalability and modularity. User interfaces, enriched with NLP capabilities, facilitate seamless interaction and data processing. Experimental results substantiate the system's efficacy, achieving a remarkable high accuracy rate in medical intelligence processing. This underscores its potential to bolster clinical decision-making by furnishing prompt and precise diagnostic insights. By diminishing reliance on manual data entry and review, the system endeavors to streamline healthcare operations, mitigate costs, and elevate overall patient care standards.

References

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Advancing Clinical Data Management with AI Techniques: A TVET Approach for Capacity Building in Nigerian Polytechnics

Abstract

References

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