The Application of Data Science Techniques and Algorithms in Women’s Health Studies

Abstract

Objective: Evaluate and map data science methods employed to solve health conditions of women, examine the problems tackled and the effectiveness. Research Method: Text analytics, science mapping, and descriptive evaluation of data science methods utilized in women-related health research. Findings: (i). The trends in scholarships using data science methods indicate gaps between women and men relating to health burden and access to health. (ii). The coronavirus (SARS-CoV-2) outbreak and the ongoing COVID-19 pandemic tend to widen the identified health gaps, increasing the disease burden for women, while reducing access to health. There are noticeable additional health burdens on pregnant women and those with several health conditions (breast cancer, gynecologic oncology, cardiovascular disease, and more). (iii). Over 95% of studies using data science methods (artificial intelligence, machine learning, novel algorithms, predictive, big data, visual analytics, clinical decision support systems, or a combination of the methods) indicate significant effectiveness. (iv). Mapping of the scientific literature to authors, sources, and countries show an upward trend; 997 (16%), 113 (1.33%), and 57 (2.63%) per article, respectively. About 95% of research utilizing data science methods in women's health studies occurred within the last four (4) years. Conclusions: The application of data science methods in tackling different health problems of women is effective and growing, and capable of easing the burden of health in women. The ongoing COVID-19 pandemic tends to compound the health burden for women more than men. Policymakers must do more to improve access to health for women.