ArticleStatistics in medicine2018
Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01864603 (Sustainable East Africa Research in Community Health), which is not on this map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Sustainable East Africa Research in Community Health
Who cites it
25 citing papers in PubMed.
- Uptake and outcomes of a novel community-based HIV post-exposure prophylaxis (PEP) programme in rural Kenya and Uganda.Journal of the International AIDS Society · 2021Trial
- Distance to clinic is a barrier to PrEP uptake and visit attendance in a community in rural Uganda.Journal of the International AIDS Society · 2019Trial
- Early Adopters of Human Immunodeficiency Virus Preexposure Prophylaxis in a Population-based Combination Prevention Study in Rural Kenya and Uganda.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2018Trial
- Utility of a machine-guided tool for assessing risk behaviour associated with contracting HIV in three sites in South Africa.Informatics in medicine unlocked · 2023Article
- Providers' Attitudes and Experiences with Pre-Exposure Prophylaxis Implementation in a Population-Based Study in Kenya and Uganda.AIDS patient care and STDs · 2022Article
- Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review.International journal of environmental research and public health · 2022Review
- Deep Ensemble Machine Learning Framework for the Estimation ofEnvironmental health perspectives · 2022Article
- Article
- The role of machine learning in HIV risk prediction.Frontiers in reproductive health · 2022Article
- Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.PloS one · 2022Article
- Revisiting performance metrics for prediction with rare outcomes.Statistical methods in medical research · 2021Article
- Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa.BMC medical research methodology · 2021Article
- HIV incidence after pre-exposure prophylaxis initiation among women and men at elevated HIV risk: A population-based study in rural Kenya and Uganda.PLoS medicine · 2021Article
- Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020Article
- Using electronic health records to identify candidates for human immunodeficiency virus pre-exposure prophylaxis: An application of super learning to risk prediction when the outcome is rare.Statistics in medicine · 2020Article
- Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data.Statistical communications in infectious diseases · 2020Article
- Understanding Demand for PrEP and Early Experiences of PrEP Use Among Young Adults in Rural Kenya and Uganda: A Qualitative Study.AIDS and behavior · 2020Article
- Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.Current HIV/AIDS reports · 2020Review
- Uptake, engagement, and adherence to pre-exposure prophylaxis offered after population HIV testing in rural Kenya and Uganda: 72-week interim analysis of observational data from the SEARCH study.The lancet. HIV · 2020Observational
- Preexposure Prophylaxis Indication Criteria Underidentify Black and Latinx Persons and Require Revision.American journal of public health · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency virus prevention program based on offering pre-exposure prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program. In this article, we consider a general class of constrained binary classification problems wherein the objective function and the constraint are both monotonic with respect to a threshold. These include the minimization of the rate of positive predictions subject to a minimum sensitivity, the maximization of sensitivity subject to a maximum rate of positive predictions, and the Neyman-Pearson paradigm, which minimizes the type II error subject to an upper bound on the type I error. We propose an ensemble approach to these binary classification problems based on the Super Learner methodology. This approach linearly combines a user-supplied library of scoring algorithms, with combination weights and a discriminating threshold chosen to minimize the constrained optimality criterion. We then illustrate the application of the proposed classifier to develop an individualized PrEP targeting strategy in a resource-limited setting, with the goal of minimizing the number of PrEP offerings while achieving a minimum required sensitivity. This proof of concept data analysis uses baseline data from the ongoing Sustainable East Africa Research in Community Health study. Copyright © 2017 John Wiley & Sons, Ltd.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.