首页|University Hospital Lausanne (CHUV) Reports Findings in Biomarkers (Advancing Rh eumatology Care Through Machine Learning)
University Hospital Lausanne (CHUV) Reports Findings in Biomarkers (Advancing Rh eumatology Care Through Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Diagnostics and Screen ing - Biomarkers is the subject of a report.According to news reporting from La usanne,Switzerland,by NewsRx editors,the research stated,"Rheumatologic dise ases are marked by their complexity,involving immune-,metabolic- and mechanica lly mediated processes which can affect different organ systems.Despite a growi ng arsenal of targeted medications,many rheumatology patients fail to achieve f ull remission." The news correspondents obtained a quote from the research from University Hospi tal Lausanne (CHUV),"Assessing disease activity remains challenging,as patient s prioritize different symptoms and disease phenotypes vary.This is also reflec ted in clinical trials where the efficacy of drugs is not necessarily measured i n an optimal way with the traditional outcome assessment.The recent COVID-19 pa ndemic has catalyzed a digital transformation in healthcare,embracing telemonit oring and patient-reported data via apps and wearables.As a further driver of d igital medicine,electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support,heralding a shift toward s patient-centered,decentralized care.Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data,promising to enhance treatment quality and patient wellbeing.Convolutional neural netwo rks (CNN) are particularly promising for radiological image analysis,aiding in the detection of specific lesions such as erosions,sacroiliitis,or osteoarthri tis,with several FDAapproved applications.Clinical predictions,including num erical disease activity forecasts and medication choices,offer the potential to optimize treatment strategies.Numeric predictions can be integrated into clini cal workflows,allowing for shared decision making with patients.Clustering pat ients based on disease characteristics provides a personalized care approach.Di gital biomarkers,such as patient-reported outcomes and wearables data,offer in sights into disease progression and therapy response more flexibly and outside p atient consultations.In association with patient-reported outcomes,disease-spe cific digital biomarkers via image recognition or single-camera motion capture e nables more efficient remote patient monitoring.Digital biomarkers may also pla y a major role in clinical trials in the future as continuous,disease-specific outcome measurement facilitating decentralized studies.Prediction models can he lp with patient selection in clinical trials,such as by predicting high disease activity.Efforts are underway to integrate these advancements into clinical wo rkflows using digital pathways and remote patient monitoring platforms.In summa ry,machine learning,digital biomarkers,and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology."
LausanneSwitzerlandEuropeBiomarker sClinical ResearchClinical Trials and StudiesCyborgsDiagnostics and Scre eningEmerging TechnologiesHealth and MedicineImmunologyMachine LearningRheumatologyTesting
NETL
NSTL
