The rapid advancements in medical technology and the increasing complexity of healthcare systems have led to a growing demand for efficient decision support systems (DSS) in personalized healthcare management. This paper explores the pivotal role of DSS in enhancing the quality and effectiveness of healthcare delivery. Personalized healthcare involves tailoring medical treatments and interventions to individual patients, which requires comprehensive data analysis and advanced computational techniques. The abstract discusses the core components of DSS, such as data mining, predictive modeling, and machine learning, and how they contribute to personalized healthcare management. Furthermore, it highlights the challenges and opportunities associated with the integration of DSS into healthcare workflows and examines the potential impact on patient outcomes and healthcare providers. The paper concludes by emphasizing the need for ongoing innovation and collaboration among stakeholders to realize the full potential of DSS in transforming healthcare delivery.
Brown, J. Decision Support Systems for Personalized Healthcare Management. Transactions on Applied Soft Computing, 2020, 2, 15. https://doi.org/10.69610/j.tasc.20201222
AMA Style
Brown J. Decision Support Systems for Personalized Healthcare Management. Transactions on Applied Soft Computing; 2020, 2(2):15. https://doi.org/10.69610/j.tasc.20201222
Chicago/Turabian Style
Brown, John 2020. "Decision Support Systems for Personalized Healthcare Management" Transactions on Applied Soft Computing 2, no.2:15. https://doi.org/10.69610/j.tasc.20201222
APA style
Brown, J. (2020). Decision Support Systems for Personalized Healthcare Management. Transactions on Applied Soft Computing, 2(2), 15. https://doi.org/10.69610/j.tasc.20201222
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