Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Abstract

A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) "women" or "men" or "sex," (2) "big data" or "artificial intelligence" or "NLP", and (3) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.