Poster Y4 - Analyzing Demographic Data Gaps in the PhysioNet Open Access Database: Toward Mitigating AI Bias in Biosignal Algorithms

Introduction: The PhysioNet Open Access Database is among the most extensive and utilized repositories of biosignal data worldwide, with 181 datasets from studies spanning the last four decades. These datasets are crucial for training and validating artificial intelligence/machine learning (AI/ML) algorithms designed to estimate and predict various health-related outcomes. However, the lack of uniform demographic reporting across these studies introduces a significant risk of bias, particularly affecting underrepresented and underserved populations. Heterogeneous reporting results in data that may not accurately represent all demographic groups, thereby skewing AI predictions toward overrepresented populations and exacerbating existing health disparities when these algorithms are deployed in real-world settings. Additionally, algorithms trained on non-representative data cannot be generalized across real-world populations, and thus are limited in their implementation in real-world healthcare settings.
Our study examines demographic reporting patterns within the PhysioNet Open Access Database to identify existing gaps and their implications. Specifically, we seek to identify existing relationships between demographic data that is more frequently reported, such as study participant age and sex, and demographic data that is frequently absent from biosignal studies, such as race and ethnicity. Additionally, even studies that do include race and/or ethnicity data do not report these data in a standardized manner, resulting in non-uniform reporting across the database. These analyses not only shed light on the prevalence of non-standardized demographic reporting within biosignal datasets, but also set the stage for evaluating the impact of such reporting on the accuracy and bias of AI/ML models in healthcare.

Materials and

Methods: The PND contains 175 studies involving human subjects, as of July 2023. We conducted a systematic analysis of these 175 biosignal datasets/databases to identify reporting patterns primarily regarding four key demographic variables: race, ethnicity, sex, and age, as well as supplementary information such as study size (N), date of publication, location, and biosignal type. Where detailed data, on the level of individual participants, was available, this data was used to calculate our values. In the case of multiple sources of data being available (i.e. from a combination of the original publication, dataset, or PhysioNet description), the PhysioNet description of the study was used.
Additionally, we conducted six pairwise Chi-square tests of independence on all permutations of two variables chosen from race, ethnicity, age, and sex to identify relationships in demographics reporting. The significance level was set to 0.05, with a Bonferroni corrected alpha of 0.0083 for 6 pairwise comparisons. A linear regression model was used to identify whether statistically significant relationships existed between the number of demographic variables reported and the date of publication and data posted on PhysioNet; however, due to substantial variation in demographics reporting between 2005-2020, these models yielded minimally successful results. Figures are generated using Python 3.9.11, NumPy 1.25.1, pandas 2.0.3, and a linear regression model was generated with scikit-learn 1.4.2.

Results, Conclusions, and Discussions: A small fraction (N=12, 6.9%) of the 175 studies involving human participants reported all four demographic variables, while 14.3% of studies (N=25) report no demographic information. Further, only 23 studies (13.1%) reported race and only 14 (8.0%) reported ethnicity, while 142 (81.1%) studies reported sex and 139 (79.2%) reported age.
Studies that reported at least two variables reported both age and sex, while no studies reporting ≤ 2 variables reported race or ethnicity. Within studies that include ethnicity data, the only reported ethnicities are latinx/non-latinx, or similar categories. We found reporting of age was not independent of reporting sex, and reporting of ethnicity was not independent of reporting race, and vice-versa (Figure 1).
Studies that report all four key demographic variables were conducted after 2015, with one exception, and primarily involved critical care patients (emergency department admitted patients or intensive care unit patients). The studies without demographic information were primarily conducted prior to 2015 (N=18). Linear regression models indicate no statistically significant relationships between year and number of variables reported by studies, as demographic reporting varies greatly after ~2005.
Figure 1. Demographic Reporting Frequency. (A): Stacked bar graph showing the reporting rates of race, ethnicity, sex, and age, as well as how many demographic variables were reported by studies. Significant associations marked by brackets and asterisks. All studies reporting race and/or ethnicity information also included both sex and age information for participants. (B) Bar graph illustrating the percentage and count of studies reporting 0-4 of the key demographic variables. (C) Heat map displaying p-values from Chi-Squared tests, with significant values marked by asterisks. P-values < 0.0083 colored according to the logarithmic scale at the right, values >0.0083 are shaded in pale blue.
Our analysis of the PND highlights significant demographic reporting gaps that risk embedding biases into AI/ML algorithms. To counteract these, there is an urgent need for the research community to lead in establishing and enforcing standards for comprehensive demographic reporting. By enhancing data collection protocols to ensure the representation of diverse populations, we can develop more accurate and fair algorithms to estimate and predict health-outcomes.

Acknowledgements (Optional): This work was supported in part by NSF CAREER #2339669.