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

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) comple

SUBMITTER: Desai PM 

PROVIDER: S-EPMC10797282 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature