ArXiv Preprint
Algorithms and technologies are essential tools that pervade all aspects of
our daily lives. In the last decades, health care research benefited from new
computer-based recruiting methods, the use of federated architectures for data
storage, the introduction of innovative analyses of datasets, and so on.
Nevertheless, health care datasets can still be affected by data bias. Due to
data bias, they provide a distorted view of reality, leading to wrong analysis
results and, consequently, decisions. For example, in a clinical trial that
studied the risk of cardiovascular diseases, predictions were wrong due to the
lack of data on ethnic minorities. It is, therefore, of paramount importance
for researchers to acknowledge data bias that may be present in the datasets
they use, eventually adopt techniques to mitigate them and control if and how
analyses results are impacted. This paper proposes a method to address bias in
datasets that: (i) defines the types of data bias that may be present in the
dataset, (ii) characterizes and quantifies data bias with adequate metrics,
(iii) provides guidelines to identify, measure, and mitigate data bias for
different data sources. The method we propose is applicable both for
prospective and retrospective clinical trials. We evaluate our proposal both
through theoretical considerations and through interviews with researchers in
the health care environment.
Chiara Criscuolo, Tommaso Dolci, Mattia Salnitri
2022-12-19
