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UID:9072@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20260622T140000
DTEND;TZID=Europe/Paris:20260622T153000
DTSTAMP:20260413T130837Z
URL:https://www.i2m.univ-amu.fr/evenements/seminaire-dantoine-moll/
SUMMARY:Antoine Moll (...): Séminaire d'Antoine Moll
DESCRIPTION:Antoine Moll: Antoine nous parlerra d'"Improving Fairness in th
 e Detection of Undiagnosed Diabetes without Increasing Loss Ratio" dont vo
 ici un résumé :\n\nUndiagnosed diabetes is a significant source of risk 
 misclassifications in life and health insurance underwriting\, affecting m
 ortality\, disability\, and critical illness assessments. Because many app
 licants are unaware of their condition\, insurers relying solely on self-r
 eported information may underestimate true medical risk. This study examin
 es how complementary screening strategies can improve the detection of und
 iagnosed diabetes while controlling medical testing costs and enhancing fa
 irness strategy of testing.\nUsing data from the National Health and Nutri
 tion Examination Survey (NHANES) linked to mortality follow-up\, several s
 creening strategies are evaluated\, ranging from simple questionnaires to 
 a model without constraints. Logistic regression and gradient boosting mod
 els are used to estimate the probability of undiagnosed diabetes\, while m
 ortality is modeled using Cox proportional hazards. Economic performance i
 s evaluated through a loss ratio framework that incorporates underwriting 
 costs.\nA screening strategy combining a medical examination with a urine 
 albumin to creatinine test markedly improves detection and mortality segme
 ntation while remaining cost efficient. Testing about 30-40% of the highes
 t risk applicants maximizes underwriting gains but it creates unfairness a
 nd methods are developed to improve fairness.
CATEGORIES:Séminaire,Statistique
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
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DTSTART:20260329T030000
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