… These findings highlight why this study focused on patients in the M … more patients than the
T segment, and why we used predictive analytics to specifically identify M segment patients at …

… important issues such as high-cost patients, triage, and clinical … the effect of HPA on patient
outcomes are not fully known. This … Healthcare predictive analytics could augment our current …

… effectiveness of predictive analytics, ultimately leading to improved patient health outcomes.
This paper contends that the integration of machine learning into predictive analytics marks …

… in the context of medical predictive analytics. … least 100 patients in each outcome group,
following current recommendations for validation studies, the study needs at least 500 patients. …

… to define both predictor and outcome variables for … Patient Patient-facing applications of
predictive analytics will need to have benefits that can be understood and are relevant to patients…

… applying predictive analytics in healthcare are described, with a review of successful applications
of predictive analytics in … interventions that target medication-related patient outcomes. …

… Work with passively collected patient outcomes is the next frontier in outcomes research. All
of these developments have laid an incredible foundation for predictive analytics to improve …

… As with the field of predictive biomarkers, however, a more formal … predictive analytics while
protecting patients—moving from tremendous predictive power to improved patient outcomes…

… systems that apply predictive analytics to improve value for high-risk patient groups. Under …
of tools to predict important outcomes, including to identify patients likely to require expensive …

… role of predictive analytics in ICUs, with a focus on its potential to enhance patient outcomes.
We … This article explores the multifaceted applications of predictive analytics in ICUs, its …