Background: Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided.
Aim: The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm.
Discussion: Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.
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http://dx.doi.org/10.3109/0284186X.2015.1062136 | DOI Listing |
Sleep Adv
November 2024
Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
Study Objectives: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in sleep quality assessment. Hence, less invasive, cost-effective, and portable alternatives need to be explored.
View Article and Find Full Text PDFStruct Dyn
November 2024
Second Target Station, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns.
View Article and Find Full Text PDFCureus
November 2024
Neurology, NeuroCareAI, Dallas, USA.
Stroke remains a critical global health challenge, with ischemic stroke comprising most cases and necessitating rapid, effective treatment to improve patient outcomes. This review explores the integration of artificial intelligence (AI) and machine learning into medical devices for stroke triaging, highlighting their impact on reducing notification times, latency in care, and health disparities. By analyzing Food and Drug Administration-approved AI-enabled devices under the "Radiological computer-assisted triage and notification software" regulation category, we assess their sensitivity, specificity, and time-to-notification as the measure of their overall effectiveness in clinical settings.
View Article and Find Full Text PDFEnviron Res
December 2024
Engler-Bunte-Institut, Water Chemistry and Water Technology, Karlsruhe Institute of Technology, Engler-Bunte-Ring 9, 76131 Karlsruhe, Germany.
This study reviewed the recovery of humic substances (HS) from anaerobic digestate of sludge as a potential fertilizer, focusing on the quantification of HS, the efficiency of HS recovery, and its interaction with pollutants. The potential pitfalls of current misunderstanding for HS quantification in sludge were pointed out. HS present in sludge showed potential to be used as a fertilizer, which solubilized insoluble phosphates for enhanced soil fertility.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
December 2024
College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected.
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