A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

Allen, Angier; Mataraso, Samson; Siefkas, Anna; Burdick, Hoyt; Braden, Gregory; Dellinger, R.P.; McCoy, Andrea J.; Pellegrini, Emily; Hoffman, Jana; Green‐Saxena, Abigail; Barnes, Gina; Calvert, Jacob; Das, Ritankar

doi:10.2196/22400

Cited by 41 publications

(44 citation statements)

References 35 publications

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“…The findings of this study have implications for the relevance of ML algorithms in population health research. Similarly, several studies have confirmed the usefulness of ML for population health research and policy decision making in various areas including child undernutrition (26) , women's height (58) , CVD risks (59) and mortality (60) as well as defining treatment effects in epidemiological studies (61) which highlights how ML is increasingly being applied to predict population health outcomes (62) . These findings may also be useful in bias reduction (60) as ML methods can accurately quantify uncertainty when data are scarce, as can be found in sub-Saharan Africa.…”

Section: Discussionmentioning

confidence: 89%

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

Bitew

Sparks

Nyarko

2021

Public Health Nutr.

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Objective: Child undernutrition is a global public health problem with serious implications. In this study, estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms. Design: This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five machine learning algorithms including eXtreme gradient boosting (xgbTree), k-nearest neighbors (K-NN), random forest (RF), neural network (NNet), and the generalized linear models (GLM) were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia. Setting: Households in Ethiopia. Participants: A total of 9,471 children below five years of age. Results: The descriptive results show substantial regional variations in child stunting, wasting, and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalized linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anemia history, child age greater than 30 months, small birth size, and maternal underweight, among others. Conclusions: The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security, and fertility regulation among others in the quest to considerably improve childhood nutrition in Ethiopia.

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Section: Discussionmentioning

confidence: 89%

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

Bitew

Sparks

Nyarko

2021

Public Health Nutr.

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“…Of the 12 studies, 5 (42%) published code used for analysis, 3 (25%) made model development code available [ 34 , 36 , 39 ], 2 (17%) published bias analysis code [ 33 , 36 ], 1 (8%) published code relevant to debiasing [ 30 ], and 1 (8%) published data selection code [ 33 ]. In addition, 1 (8%) study used publicly available code for analysis [ 31 ], and code was specified as available upon request in 1 (8%) study [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Huang¹,

Galal²,

Etemadi³

et al. 2022

JMIR Med Inform

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Background Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear. Objective Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML. Methods A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase databases, as well as Google Scholar, identified 635 records, of which 12 studies were included. Results Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8%) described a model in routine clinical use, 2 (17%) examined prospectively validated clinical models, and the remaining 9 (75%) described internally validated models. In addition, 8 (67%) studies concluded that racial bias was present, 2 (17%) concluded that it was not, and 2 (17%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42%), accuracy (4/12, 25%), and disparate impact (2/12, 17%). All 8 (67%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors’ chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them. Conclusions The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.

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“…48 ML has also been used to identify when standard scoring systems accentuate racial disparities, and models have been designed with the aim of reducing racial bias in outcome predictions. 49 In addition to bias and computational challenges, ML projects introduce the same challenges of any interdisciplinary research project aiming to inform practice and policy. Developing a practical model requires expertise from healthcare epidemiologists, clinicians, computer scientists, and other professionals.…”

Section: Discussionmentioning

confidence: 99%

Machine learning and artificial intelligence: applications in healthcare epidemiology

Hamilton

Strauss

Martínez

et al. 2021

ASHE

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Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

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A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

Cited by 41 publications

References 35 publications

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Machine learning and artificial intelligence: applications in healthcare epidemiology

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