What question did this study set out to answer?

The aim is to assess how integrating patient-generated health data with human-guided machine learning can improve model explainability and better reflect patient voices.

May 9, 2026Journal of Clinical and Translational Science0 citationsOpen Access

520 Integrating patient voices to enhance patient-reported outcomes: A human-guided machine learning approach to analyzing health narratives

BABrenda Amezquita-Castro

Key Points

The aim is to assess how integrating patient-generated health data with human-guided machine learning can improve model explainability and better reflect patient voices.
Data were collected in a randomized control trial (NCT00505310) on renal cancer patients.
Patients completed four journal entries over 10 days addressing health behaviors and emotions related to cancer.
The evaluation compared standard ML models with human-guided ML models for characterizing unstructured patient data.
Results will demonstrate how a patient-centered approach enhances understanding of diverse factors affecting patient outcomes.
The research anticipates generating insights that address biases in machine learning applications in health.
The study aims to provide strategies for incorporating patient experiences into health research effectively.

Abstract

Objectives/Goals: To evaluate the extent to which characterization of unstructured patient-generated health data (UPGD) (e.g., health journals) with human-guided machine learning (HGML) improves model explainability and integrates patient voices among renal cancer patients in a randomized control trial (RCT). Methods/Study Population: Data were collected in an RCT (NCT00505310) assessing the benefits of an emotional expression intervention on renal cancer patients’ quality-of-life outcomes. Over a 10-day period, patients completed four journal-type written responses to structured prompts about their health lifestyle behaviors or deepest thoughts and feelings linked to cancer. I will 1) evaluate the extent to which a standard ML model versus an HGML model aligns with principles of trustworthy health AI when characterizing UPGD, 2) evaluate the extent to which UPGD captures condition-specific data complementary to PROMs, and 3) extract journey maps from narratives about patient experiences navigating cancer care. Results/Anticipated Results: Results will illustrate a patient-centered value research approach that centers patient perspectives, contextualizes heterogeneous factors influencing patient outcomes, and mitigates biases in the health research machine learning pipeline. Discussion/Significance of Impact: This research contributes to the development of evidence-based and empirically validated strategies for health researchers to incorporate explainable HGML tools that facilitate the inclusion of patients’ experiences, perspectives, needs, and priorities throughout the research process.

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