In this work, we study the optimal design of two-armed clinical trials to maximize the accuracy of parameter estimation in a statistical model, where the interaction between patient covariates and treatment are explicitly incorporated to enable precision medication decisions. Such a modeling extension leads to significant complexities for the produced optimization problems because they include optimization over design and covariates concurrently. We take a min-max optimization model and minimize (over design) the maximum (over population) variance of the estimated interaction effect between treatment and patient covariates. This results in a min-max bilevel mixed integer nonlinear programming problem, which is notably challenging to solve. To address this challenge, we introduce a surrogate optimization model by approximating the objective function, for which we propose two solution approaches. The first approach provides an exact solution based on reformulation and decomposition techniques. In the second approach, we provide a lower bound for the inner optimization problem and solve the outer optimization problem over the lower bound. We test our proposed algorithms with synthetic and real-world data sets and compare them with standard (re)randomization methods. Our numerical analysis suggests that the proposed approaches provide higher-quality solutions in terms of the variance of estimators and probabilitymore »
An Activity Recognition System for Taking Medicine Using In-The-Wild Data to Promote Medication Adherence
Nearly half of people prescribed medication to treat chronic or short-term conditions do not take their medicine as prescribed. This leads to worse treatment outcomes, higher hospital admission rates, increased healthcare costs, and increased morbidity and mortality rates. While some instances of medication non-adherence are a result of problems with the treatment plan or barriers caused by the health care provider, many are instances caused by patient-related factors such as forgetting, running out of medication, and not understanding the required dosages. This presents a clear need for patient-centered systems that can reliably increase medication adherence. To that end, in this work we describe an activity recognition system capable of recognizing when individuals take medication in an unconstrained, real-world environment. Our methodology uses a modified version of the Bagging ensemble method to suit unbalanced data and a classifier trained on the prediction probabilities of the Bagging classifier to identify when individuals took medication during a full-day study. Using this methodology we are able to recognize when individuals took medication with an F-measure of 0.77. Our system is a first step towards developing personal health interfaces that are capable of providing personalized medication adherence interventions.
- Award ID(s):
- 1952236
- Publication Date:
- NSF-PAR ID:
- 10294629
- Journal Name:
- IUI '21: 26th International Conference on Intelligent User Interfaces
- Page Range or eLocation-ID:
- 575 to 584
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3397481.3450673
