Skip to content!

Publications of the Project: Antibiotic Resistance: Socio-Economic Determinants and the Role of Information and Salience in Treatment Choice (ABRSEIST)

Go to page
remove add
2 results, from 1
  • Diskussionspapiere 1911 / 2020

    Machine Predictions and Human Decisions with Variation in Payoffs and Skills

    Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in decision maker payoff functions ...

    2020| Michael Allan Ribers, Hannes Ullrich
  • Diskussionspapiere 1803 / 2019

    Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

    Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and microbiological laboratory data from Denmark to train a machine learning algorithm predicting bacterial causes of urinary tract infections. Based on predictions, we develop policies to improve ...

    2019| Michael A. Ribers, Hannes Ullrich
2 results, from 1