首页|McGill University Reports Findings in Psoriasis (Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populati onal Study from Quebec, Canada)
McGill University Reports Findings in Psoriasis (Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populati onal Study from Quebec, Canada)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Skin Diseases and Cond itions - Psoriasis is the subject of a report. According to news reporting out o f Montreal, Canada, by NewsRx editors, research stated, "Psoriasis is a major gl obal health burden affecting 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consu mption, smoking, exercise) and characteristics as drivers of psoriasis risk." Our news journalists obtained a quote from the research from McGill University, "However, it is increasingly recognized that health behavior arises in the conte xt of larger social, cultural, economic and environmental determinants of health . We aimed to identify the top risk factors that significantly impact the incide nce of psoriasis at the neighborhood level using populational data from the prov ince of Quebec (Canada) and advanced tree-based machine learning (ML) techniques . Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1 year prior to psori asis onset were obtained from the Canadian Urban Environment Health Consortium ( CANUE) and Statistics Canada (StatCan) and were input as predictors into the gra dient boosting ML. Model performance was evaluated using the area under the curv e (AUC). Parsimonious models and partial dependence plots were determined to ass ess directionality of the relationship. The incidence of psoriasis varied geogra phically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious mode l (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amo ngst top predictors, ultraviolet (UV) radiation, maximum daily temperature, prop ortion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggeste d a higher psoriasis incidence in the middle socioeconomic class neighborhoods."
MontrealCanadaNorth and Central Amer icaCyborgsDermatologyEmerging TechnologiesHealth and MedicineMachine L earningPapulosquamous Skin Diseases and ConditionsPsoriasisRisk and Preven tionSkin Diseases and Conditions
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