首页|National Institute of Astrophysics Reports Findings in Rheumatoid Arthritis (Mac hine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review)
National Institute of Astrophysics Reports Findings in Rheumatoid Arthritis (Mac hine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Autoimmune Diseases an d Conditions - Rheumatoid Arthritis is the subject of a report. According to new s reporting from Puebla, Mexico, by NewsRx journalists, research stated, “This s tudy aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions. This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist.” The news correspondents obtained a quote from the research from the National Ins titute of Astrophysics, “We searched PubMed, Cochrane Library, Web of Science, S copus, and EBSCO databases for cohort studies that derived and/or validated ML m odels focused on predicting rheumatoid arthritis (RA) treatment response. We ext racted data and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPO D) and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. From 210 unduplicated records identified by the literature search, we retained 29 eli gible studies. Of these studies, 10 developed a predictive model and reported a mean adherence to the TRIPOD guidelines of 45.6 % (95 % CI: 38.3-52.8 %). The remaining 19 studies not only developed a pre dictive model but also validated it externally, with a mean adherence of 42.9 % (95 % CI: 39.1-46.6 %). Most of the articles had an u nclear risk of bias (41.4 %), followed by a high risk of bias, whic h was present in 37.9 %. In recent years, ML methods have been incr easingly used to predict treatment response in RA.”
PueblaMexicoNorth and Central Americ aArthritisAutoimmune Diseases and ConditionsCyborgsEmerging TechnologiesHealth and MedicineJoint Diseases and ConditionsMachine LearningMusculos keletal Diseases and ConditionsRheumatoid Arthritis
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