Health Citizens – European Research Center

Tag: Predictive Models

  • Artificial intelligence bias in the prediction and detection of cardiovascular disease

    Artificial intelligence bias in the prediction and detection of cardiovascular disease

    AI can help identify people at risk of cardiovascular disease, detect it early and allow timely intervention. However, bias can arise at any step in the development, validation and use of these algorithms, causing them to perform poorly for historically marginalised groups. This article discusses the sources and consequences of AI bias in predicting and detecting cardiovascular disease, and presents strategies to reduce it.

    Bias can come from training data that does not represent the population a tool is meant for, from measurements or devices that work less well for some groups, and from leaving out important factors such as social determinants of health. It can also reflect inequalities in care: because women and ethnic minority patients are more likely to have their cardiovascular disease missed or diagnosed late, algorithms trained on this data may be less accurate at detecting it in these groups. Unequal access to digital technologies can further limit how well some groups are represented in the data. The authors give several examples of AI models that performed worse for women, Black patients or older adults.

    To reduce bias, the authors propose an AI health equity framework covering the whole lifecycle of an algorithm: diverse research teams, early involvement of patients, representative and balanced data, the inclusion of social determinants of health, input from clinicians, validation across different groups, transparent reporting of results and monitoring after implementation. They conclude that, without these measures, AI bias could worsen existing healthcare inequities and miss opportunities to predict and detect cardiovascular disease.

    Learn more about this article here: https://doi.org/10.1038/s44325-024-00031-9

    Reference:

    Mihan, A., Pandey, A., & Van Spall, H.G.C. (2024). Artificial intelligence bias in the prediction and detection of cardiovascular disease. npj Cardiovascular Health, 1, 31.

  • Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

    Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

    Machine learning models can help predict which patients with diabetes are likely to be readmitted to hospital, but they need to work fairly for all patients. This study compared several of these models, including deep learning, generalised linear models, gradient boosting machines and naive Bayes, assessing not only their accuracy but also how fair their predictions were across age, gender and race.

    The gradient boosting model performed best overall, with an accuracy of 82.2% and consistently low error rates for most racial and gender groups. However, across several of the models, African American and Hispanic patients had higher rates of false positives, meaning they were more likely to be wrongly predicted to be readmitted. No single model performed best for every group: some worked better for certain groups of patients and others for other groups.

    The authors conclude that there is no one-size-fits-all solution, and that models need to be chosen and adjusted carefully to ensure they are both accurate and fair for all patients. According to them, fair algorithms can help reduce disparities and improve outcomes for patients with diabetes from all backgrounds.

    Learn more about this study here: https://doi.org/10.1109/ICHI61247.2024.00105

    Reference:

    Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024). Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) (pp. 660–669). IEEE.

  • Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

    Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

    Stroke is a leading cause of death and serious long-term disability in the US, with a particularly high risk among Black individuals. Risk prediction tools help identify who would benefit most from prevention, but they need to work equally well for everyone. This study compared several of these tools, including two stroke-specific models, a general cardiovascular risk model and two new machine learning models, using data from 62,482 Black and White adults aged 45 and over from four large US studies.

    Overall, the models performed similarly, and the new machine learning models did not improve their accuracy. However, all of them were worse at distinguishing between people who would and would not have a stroke when applied to Black individuals. Among women, the models’ accuracy score was 0.76 for White women and 0.69 for Black women. Among men, it ranged from 0.71 to 0.72 for White men and from 0.64 to 0.66 for Black men. The models also performed better in adults under 60 than in older adults.

    The authors conclude that current approaches are not enough to close this gap, and that the range of risk factors considered and the modelling techniques used need to be improved to address these racial disparities.

    Learn more about this study here: https://doi.org/10.1001/jama.2022.24683

    Reference:

    Hong, C., Pencina, M.J., Wojdyla, D.M., Hall, J.L., Judd, S.E., Cary, M., Engelhard, M.M., Berchuck, S., Xian, Y., D’Agostino, R., Howard, G., Kissela, B., & Henao, R. (2023). Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups. JAMA, 329(4), 306–317.