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Updated: May 22, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection
Hendrik Schäfer1,2, Nesrine Lajmi3, Paolo Valente1
1Clinical Development & Medical Affairs, Roche Diagnostics International Ltd., Forrenstrasse 2, 6343 Rotkreuz, Switzerland.
Abstract:
In a rapidly changing technology landscape, "Clinical Decision Support" (CDS) has become an important tool to improve patient management. CDS systems offer medical professionals new insights to improve diagnostic accuracy, therapy planning, and personalized treatment. In addition, CDS systems provide cost-effective options to augment conventional screening for secondary prevention. This review aims to (i) describe the purpose and mechanisms of CDS systems, (ii) discuss different entities of algorithms, (iii) highlight quality features, and (iv) discuss challenges and limitations of CDS in clinical practice. Furthermore, we (v) describe contemporary algorithms in oncology, acute care, cardiology, and nephrology. In particular, we consolidate research on algorithms across diseases that imply a significant disease and economic burden, such as lung cancer, colorectal cancer, hepatocellular cancer, coronary artery disease, traumatic brain injury, sepsis, and chronic kidney disease.
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