Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

By M. Gianfrancesco, Suzanne Tamang, J. Yazdany, G. Schmajuk. JAMA Internal Medicine, 2018

1,183 citations recorded by the source corpus.

A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/161ce338538f94b0b9be51ae2336db0aa4b012e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Informatics

In Health Information Management

Citations in the corpus

Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers (2019, methodology): “Since the performance of an AI algorithm is strongly dependent upon its training data, there is a genuine risk that AI algorithms may not perform well in real-world practice and that an algorithm trained at one institution provides inaccurate outputs when applied to data at another institution (9, 1”
A validation of machine learning-based risk scores in the prehospital setting (2019, methodology): “Perhaps most concerningly, ML methods readily integrate undesirable systemic biases present in the data they are trained on into the prediction model [31].”
MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care (2020, background): “” New reports suggest that biases hidden in the training data used for model development could have negative consequences in certain populations.(8,9) It is clear that the performance of any AI model broadly depends on its reliability and its ability to generalize to the setting and population in wh”
Ensuring Fairness in Machine Learning to Advance Health Equity (2018, methodology): “For example, the data may not contain enough examples from a group to properly tailor the predictions to them (minority bias) (28), or the data set of the protected group may be less informative because features are missing not at random as a result of more fragmented care (29, 30).”
Bias in artificial intelligence algorithms and recommendations for mitigation (2023, background): “This can occur when patients receive different care or are incorrectly diagnosed based on sociodemographic factors reflecting practitioner bias [28].”
Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (2020, background): “In addition, institutions record patient information differently; as a result, if AI models trained at one institution are implemented to analyze data at another institution, this may result in errors [52].”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “ML is becoming the transformational force in healthcare (89) for guiding individual and population health with several computational benefits, which includes realtime patient monitoring; disease patterns analysis (90); disease diagnosis and prescription of medicines; patientcentric care provision wi”
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, background): “Whilst AI might appear to be objective, biases can be inherent in the algorithms (Gianfrancesco et al., 2018; Rajkomar et al., 2018).”