Unbiased Pain Assessment through Wearables and EHR Data: Multi-attribute
  Fairness Loss-based CNN Approach
Sharmin Sultana Md Mahmudur Rahman Atqiya Munawara Mahi Shao-Hsien Liu Mohammad Arif Ul Alam
The combination of diverse health data (IoT, EHR, and clinical surveys) and
scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of
physical, behavioral, and psycho-social indicators of pain status. Despite the
hype and promise to fundamentally alter the healthcare system with
technological advancements, much AI adoption in clinical pain evaluation has
been hampered by the heterogeneity of the problem itself and other challenges,
such as personalization and fairness. Studies have revealed that many AI (i.e.,
machine learning or deep learning) models display biases and discriminate
against specific population segments (such as those based on gender or
ethnicity), which breeds skepticism among medical professionals about AI
adaptability. In this paper, we propose a Multi-attribute Fairness Loss (MAFL)
based CNN model that aims to account for any sensitive attributes included in
the data and fairly predict patients' pain status while attempting to minimize
the discrepancies between privileged and unprivileged groups. In order to
determine whether the trade-off between accuracy and fairness can be satisfied,
we compare the proposed model with well-known existing mitigation procedures,
and studies reveal that the implemented model performs favorably in contrast to
state-of-the-art methods. Utilizing NIH All-Of-US data, where a cohort of 868
distinct individuals with wearables and EHR data gathered over 1500 days has
been taken into consideration to analyze our suggested fair pain assessment
system.