Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/298444 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
CEBI Working Paper Series No. 24/22
Versionsangabe: 
Revised: January 31, 2024
Verlag: 
University of Copenhagen, Department of Economics, Center for Economic Behavior and Inequality (CEBI), Copenhagen
Zusammenfassung: 
We consider the effects of 'precision' screening policies for cancer guided by algorithms. We first show that complex machine learning models can indeed predict cancer better than simpler models that use established risk factors. We then tackle the evaluation challenge: an algorithm that can predict cancer in a hold-out set only establishes predictability; it does not imply an algorithmic screening rule built on it would improve social welfare. Using a series of policy evaluation methods we show that targeting screening via algorithm could in fact lead to large health benefits. Moreover, we show the choice of prediction target is key - not all models with high accuracy can be used to construct beneficial screening policies.
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
950.27 kB





Publikationen in EconStor sind urheberrechtlich geschützt.