首页|Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medic ine Reports Findings in Personalized Medicine (A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort s tudy ...)
Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medic ine Reports Findings in Personalized Medicine (A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort s tudy ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research st ated, “Endometriosis (EMs) and adenomyosis (AD) are common gynecological disease s that impact women’s health, and they share symptoms such as dysmenorrhea, chro nic pain, and infertility, which adversely affect women’s quality of life. Curre nt diagnostic approaches for EMs and AD involve invasive surgical procedures, an d thus, methods of noninvasive differentiation between EMs and AD are needed.” Our news journalists obtained a quote from the research from the Shuguang Hospit al Affiliated to Shanghai University of Traditional Chinese Medicine, “This retr ospective cohort study introduces a novel, noninvasive classification methodolog y employing a stacked ensemble machine learning (ML) model that utilizes periphe ral blood and coagulation markers to distinguish between EMs and AD. The study i ncluded a total of 558 patients (329 with EMs and 229 with AD), in whom key hema tological and coagulation markers were analyzed to identify distinctive profiles . Feature selection was conducted through ML (logistic regression, support vecto r machine, and K-nearest neighbors) to determine significant hematological marke rs. Red cell distribution width, mean corpuscular hemoglobin concentration, acti vated partial thromboplastin time, international normalized ratio, and antithrom bin III were proved to be the key distinguishing indexes for disease differentia tion. Among all the ML classification models developed, the stacked ensemble mod el demonstrated superior performance (area under the curve = 0.803, 95% credibility interval = 0.701-0.904). Our findings demonstrate the effectiveness of the stacked ensemble ML model for classifying EMs and AD. Integrating biomark ers into this multi-algorithm framework offers a novel approach to noninvasive d iagnosis.”
ShanghaiPeople’s Republic of ChinaAs iaBiomarkersClinical ResearchClinical Trials and StudiesCyborgsDiagnos tics and ScreeningDrugs and TherapiesEmerging TechnologiesEndometriosisF emale Genital Diseases and ConditionsFemale Urogenital Diseases and ConditionsHealth and MedicineMachine LearningPersonalized MedicinePersonalized The rapyUterine Diseases and ConditionsWomen’s Health
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