Personalized Diabetes Management Using Electronic Medical Records

By Dimitris J. Bertsimas, Nathan Kallus, Alexander M. Weinstein, Ying Daisy Zhuo. 2016

138 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Health Information Management

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Citations in the corpus

Balanced Policy Evaluation and Learning (2017, background): “The aim is to transform electronic health records to personalized treatment regimes [6], transactional records to personalized pricing strategies [5], and click- and “like”-streams to personalized advertising campaigns [8] – problems of great practical significance.”
Optimal trees for prediction and prescription (2018, methodology): “Since the counterfactuals are not known for this example we again employ the nearestneighbor matching algorithm from [19] to impute the counterfactual values on the test set.”
Learning to Personalize from Observational Data (2016, background): “For example, in medicine, there is a vast literature on predicting patient-specific responses to treatment [6, 7] and picking the best by comparing predictions [8, 9], although recent work [10] has looked at alternative approaches for the case of randomized, experimental data.”
The Price of Interpretability (2019, background): “Predictive models are used in an increasingly high-stakes set of applications, from bail decisions in the criminal justice system [2, 23] to treatment recommendations in personalized medicine [4].”
Recursive Partitioning for Personalization using Observational Data (2016, background): “For example, in medicine, there is a vast literature on predicting patient-speciﬁc responses to treatment (Feldstein et al., 1978; Stoehlmacher et al., 2004) and picking the best by comparing (Qian & Murphy, 2011; Bertsimas et al., 2017).”
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, methodology): “The approaches taken by such methods to estimate the treatment effects involve either building a separate regression model for each treatment (Stoehlmacher et al., 2004; Qian and Murphy, 2011; Bertsimas et al., 2017) or using the treatment as a feature and adjusting for the imbalance between the dif”
Policy Evaluation with Latent Confounders via Optimal Balance (2019, background): “Personalized intervention policies are of increasing importance in education [32], healthcare [3], and public policy [26].”