Optimizing Healthcare Outcomes through Data-Driven Predictive Modeling  

作　　者：Md Nagib Mahfuz Sunny Mohammad Balayet Hossain Sakil Abdullah Al Nahian Syed Walid Ahmed Md Newaz Shorif Jennet Atayeva Md Nagib Mahfuz Sunny;Mohammad Balayet Hossain Sakil;Abdullah Al Nahian;Syed Walid Ahmed;Md Newaz Shorif;Jennet Atayeva(Department of Engineering & Technology, Trine University, Detroit, USA;MBBS, Childrens Clinic of Michigan, Hamtramck, USA;Department of Graduate & Professional Studies, Trine University, Detroit, USA)

机构地区：[1]Department of Engineering & Technology, Trine University, Detroit, USA [2]MBBS, Childrens Clinic of Michigan, Hamtramck, USA [3]Department of Graduate & Professional Studies, Trine University, Detroit, USA

出　　处：《Journal of Intelligent Learning Systems and Applications》2024年第4期384-402,共19页智能学习系统与应用（英文）

摘　　要：This study investigates the transformative potential of big data analytics in healthcare, focusing on its application for forecasting patient outcomes and enhancing clinical decision-making. The primary challenges addressed include data integration, quality, privacy issues, and the interpretability of complex machine-learning models. An extensive literature review evaluates the current state of big data analytics in healthcare, particularly predictive analytics. The research employs machine learning algorithms to develop predictive models aimed at specific patient outcomes, such as disease progression and treatment responses. The models are assessed based on three key metrics: accuracy, interpretability, and clinical relevance. The findings demonstrate that big data analytics can significantly revolutionize healthcare by providing data-driven insights that inform treatment decisions, anticipate complications, and identify high-risk patients. The predictive models developed show promise for enhancing clinical judgment and facilitating personalized treatment approaches. Moreover, the study underscores the importance of addressing data quality, integration, and privacy to ensure the ethical application of predictive analytics in clinical settings. The results contribute to the growing body of research on practical big data applications in healthcare, offering valuable recommendations for balancing patient privacy with the benefits of data-driven insights. Ultimately, this research has implications for policy-making, guiding the implementation of predictive models and fostering innovation aimed at improving healthcare outcomes.This study investigates the transformative potential of big data analytics in healthcare, focusing on its application for forecasting patient outcomes and enhancing clinical decision-making. The primary challenges addressed include data integration, quality, privacy issues, and the interpretability of complex machine-learning models. An extensive literature review evaluates the current state of big data analytics in healthcare, particularly predictive analytics. The research employs machine learning algorithms to develop predictive models aimed at specific patient outcomes, such as disease progression and treatment responses. The models are assessed based on three key metrics: accuracy, interpretability, and clinical relevance. The findings demonstrate that big data analytics can significantly revolutionize healthcare by providing data-driven insights that inform treatment decisions, anticipate complications, and identify high-risk patients. The predictive models developed show promise for enhancing clinical judgment and facilitating personalized treatment approaches. Moreover, the study underscores the importance of addressing data quality, integration, and privacy to ensure the ethical application of predictive analytics in clinical settings. The results contribute to the growing body of research on practical big data applications in healthcare, offering valuable recommendations for balancing patient privacy with the benefits of data-driven insights. Ultimately, this research has implications for policy-making, guiding the implementation of predictive models and fostering innovation aimed at improving healthcare outcomes.

关 键 词：Big Data Analytics Predictive Analytics Healthcare Clinical Decision-Making Data Quality PRIVACY 

分 类 号：R73[医药卫生—肿瘤]