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Enhancing Patient Care with Machine Learning

Authors:
  • Leading Health Insurance

Abstract

Abstract: This article delves into the transformative potential of machine learning ML within the healthcare sector, addressingpersistent challenges like rising costs, discrepancies in care quality, and the urgency for accurate diagnoses. Through a detailedexploration, it demonstrates how ML enhances diagnostic precision, personalizes patient care, and streamlines efficiency, marking asignificant shift towards proactive, data - informed healthcare. The document underscores the importance of overcoming obstacles suchas data privacy concerns, infrastructure requirements, and the need for cross - disciplinary cooperation to fully harness MLs capabilities.Highlighting initiatives like the Biden Cancer Moonshot, it emphasizes a collaborative approach to integrating ML in healthcare, aimingto improve patient outcomes significantly. Furthermore, it outlines a multi - faceted solution, incorporating predictive analytics,personalized medicine, and improved operational efficiency, thereby advocating for a patient - centered, data - driven healthcare system that leverages ML for better health outcomes and quality of life.
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Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.