首页|Study Results from Royal Wolverhampton NHS Foundation Trust Update Understanding of Machine Learning [Machine Learning for Patient-Based Real -Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory]
Study Results from Royal Wolverhampton NHS Foundation Trust Update Understanding of Machine Learning [Machine Learning for Patient-Based Real -Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Wolverhampton, United Kingd om, by NewsRx correspondents, research stated, "The rapidly evolving field of ma chine learning (ML), along with artificial intelligence in a broad sense, is rev olutionising many areas of healthcare, including laboratory medicine." The news reporters obtained a quote from the research from Royal Wolverhampton N HS Foundation Trust: "The amalgamation of the fields of ML and patient-based rea l-time quality control (PBRTQC) processes could improve the traditional PBRTQC a nd error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-syste matic errors, and combinations of different types of errors in clinical laborato ries. The studies discussed used ML for detecting bias, the requirement for re-c alibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validator s or traditional PBRTQC algorithms."
Royal Wolverhampton NHS Foundation TrustWolverhamptonUnited KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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