2020
DOI: 10.1146/annurev-publhealth-040119-094437
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Machine Learning in Epidemiology and Health Outcomes Research

Abstract: Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-… Show more

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Cited by 156 publications
(132 citation statements)
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“…Machine learning methods can learn complex structures by incorporating numerous variables with high dimensional data [9]. Excellent performance of these methods has been validated in health service [10] and health outcomes studies [11]. Regularized logistic regression (RLR), as the fundamental and most commonly used machine learning method, is a generalized linear regression model for probability analysis.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning methods can learn complex structures by incorporating numerous variables with high dimensional data [9]. Excellent performance of these methods has been validated in health service [10] and health outcomes studies [11]. Regularized logistic regression (RLR), as the fundamental and most commonly used machine learning method, is a generalized linear regression model for probability analysis.…”
Section: Introductionmentioning
confidence: 99%
“…There has been an increase in the application of ML techniques to medicine and other research areas [ 37 ], but there is a paucity of the use of ML techniques in tobacco research. ML is a natural extension of traditional statistical approaches that becomes increasing valuable as the amount of data increases and the dimensionality of the dataset increases [ 38 ].…”
Section: Introductionmentioning
confidence: 99%
“…On the contrary, precision medicine, AI, and our deep understanding of disease conditions offer a great opportunity to save resources for those countries that have practiced a one-size-fits all and a piecemeal approach in their public health thinking and programming and have not reaped adequate return for their investments. Chronic diseases, and their multifactorial nature, the advent of technological advancements in the form of AI, and the 'precision' in precision medicine have the potential to redefine and replace conventional public health approaches with a new holistic paradigm [148]. There remains a huge scope for introducing educational programmes, developing policies, and strengthening systems to capitalize on the rapid development in the field and customize activities for collectives (persons who share common traits and characteristics) rather than communities.…”
Section: Resultsmentioning
confidence: 99%