Date of Publication :25th July 2024

Abstract:This paper presents a novel methodology aimed at refining the classification of Electronic Health Record (EHR) text, focusing on overcoming challenges posed by unstructured narratives and complex abbreviations. Our approach emphasizes improving predictive precision in patient diagnosis by strategi- cally organizing critical elements such as Symptoms, History, and Medications within the EHR framework. Leveraging Convolu- tional Neural Networks (CNNs) and the GloVE pretrained model, our study aims to discern intricate features in medical summaries, potentially reshaping the landscape of healthcare data classifi- cation. By incorporating insights from recent advancements in health informatics, including the utilization of Health Record Summaries and the integration of deep learning techniques, our methodology seeks to bolster the efficiency and efficacy of patient care practices. Through keyword extraction from the database, and model architecture design, we strive to enhance deep learning models capabilities in healthcare tasks such as clustering patients based on common features. Additionally, our exploration of automated medical record labeling using the MT Samples Dataset underscores the potential of deep learning in addressing the challenges of handling vast amounts of healthcare data. By integrating rule-based, machine learning, and hybrid approaches, and emphasizing feature selection for optimal section recognition, our methodology offers a comprehensive framework for advancing clinical predictive analytics and processing volu- minous health-related data. Overall, this paper contributes to the ongoing efforts in leveraging cutting-edge technologies to improve healthcare data management and patient care outcomes.

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