Visualizing machine learning-based predictions of postpartum depression risk for lay audiences

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 31; no. 2; pp. 289 - 297
• Main Authors: Desai, Pooja M, Harkins, Sarah, Rahman, Saanjaana, Kumar, Shiveen, Hermann, Alison, Joly, Rochelle, Zhang, Yiye, Pathak, Jyotishman, Kim, Jessica, D’Angelo, Deborah, Benda, Natalie C, Reading Turchioe, Meghan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 18.01.2024
• Subjects: Adolescent; Adult; Data Visualization; Depression, Postpartum - diagnosis; Female; Humans; Middle Aged; Research and Applications; Risk Factors; Surveys and Questionnaires; Young Adult; data visualization; postpartum depression; machine learning; health communication
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1093/jamia/ocad198

Abstract Objectives To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes). Materials and methods We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome. Results Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%). Discussion and conclusion All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.