Sustainable Development in Medical Applications Using Neural Network Architecture

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Shuyi Jiang


The purpose of this research is to propose a methodology utilizing machine learning techniques to support medical organizations in effectively managing risks. Specifically, the study aims to connect social media data to identify and assess potential threats, ultimately enabling healthcare management to make informed decisions for their organizations and clients. The research employs machine learning algorithms to analyze user-generated content on social media platforms, generating comprehensive visual representations of various risk categories and their magnitudes. Additionally, the study utilizes data simplification techniques, including categorization, to streamline data processing and enhance overall efficiency. A computational framework is also developed, incorporating closed-form connections for threat assessment and evaluation. The study further empirically analyses the Consumer Value Stores (CVS) established for medical care in the United States. The findings reveal that prevalent threats within the lower quartile of client messages about CVS services include operational, financial, and technological risks. The severity of these risks is distributed among high risk (21.8%), moderate risk (78%), and minimal risk (0.2%). The research also presents several metrics to demonstrate the robustness of the proposed framework, confirming its effectiveness in effectively identifying and addressing potential threats. This research provides insights that can help healthcare management make informed decisions and foster a safer and more secure environment for their organizations and the people they serve.

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Special Issue - Next generation Pervasive Reconfigurable Computing for High Performance Real Time Applications