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

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Area of Science:

  • Health Informatics
  • Medical Decision Making
  • Patient Communication

Background:

  • Machine learning (ML) models are increasingly used to predict health risks, such as postpartum depression (PPD).
  • Effective communication of these ML-derived risk scores to patients is crucial for informed decision-making and care engagement.
  • The optimal format for presenting complex health information, especially risk scores, remains an area of active research.

Purpose of the Study:

  • To evaluate how different visual and textual formats for presenting ML-derived PPD risk scores influence patient understanding and actions.
  • To assess the impact of these formats on primary outcomes (action classification) and secondary outcomes (intention to seek care, perceived risk, trust, and preferences).

Main Methods:

  • An online survey was administered to 504 English-speaking females aged 18-45.
  • Participants were exposed to ML-derived PPD risk information presented in four formats: text only, numeric only, gradient number line, and segmented number line.
  • Data were collected on patient classification of recommended actions, intention to seek care, perceived risk, trust, and format preferences.

Main Results:

  • Patient accuracy in classifying recommended actions was high (93%) across all presentation formats.
  • Increasing risk severity significantly enhanced perceived risk, trust in healthcare providers, and agreement with treatment recommendations.
  • While all formats yielded high classification accuracy, there were inconsistencies in how formats affected perceived risk, trust, and behavioral intentions; the gradient number line was the most preferred format (43%).

Conclusions:

  • While presentation format did not significantly alter the accuracy of patient classification for ML-derived PPD risk scores, the level of risk presented is a key driver of patient perception and engagement.
  • Healthcare providers and researchers should carefully select data visualization methods based on the intended patient outcome, considering that gradient number lines may enhance patient preference.
  • Future research should explore nuanced differences in how various formats influence patient trust and behavioral intentions for ML-driven health predictions.