Artificial intelligence and patient narratives: A novel approach to assessing hope in patients with cancer

TL;DR

This study shows that AI can analyze patient-written texts to predict hope levels in cancer patients, which correlates with a standard hope assessment tool.

Contribution

A novel method using AI to assess hope in cancer patients through their written narratives.

Findings

AI-predicted hope levels were independently associated with hope index scores (s-ATHS).

Patients from one center showed higher hope levels compared to others.

Poorer ECOG performance status was linked to lower hope scores.

Abstract

The present study aimed to evaluate the feasibility of patient-generated text and its interpretation by artificial intelligence (AI) as a valid correlate of hope levels in patients with cancer. For this purpose, four medical centers recruited consecutive patients with cancer and the patients were administered a questionnaire to collect data on patient characteristics and a shortened version of the Adult Trait Hope Scale (s-ATHS). Additionally, all participants provided written text on their hope levels, which was then analyzed by a deep neural network model. AI predicted hope labels, as well as numerous patient, disease and center features which were then associated with the scores from s-ATHS using univariate and multivariate gamma regression analyses. The present study comprised 461 patients with cancer, 194 (42.1%) of whom had metastatic disease. Multivariate gamma regression analysis identified three variables independently associated with hope index scores (s-ATHS): Treatment center (B=-0.09, Wald=4.77, P=0.029), Eastern Cooperative Oncology Group (ECOG) performance status (B=-0.09, Wald=47.41, P<0.001) and AI-predicted hope level (B=0.06, Wald=44.24, P<0.001). The results revealed that cases from one of the centers in the present study, a university hospital located in a different city than the other centers, exhibited higher hope levels. Additionally, a poorer ECOG performance status and lower AI-predicted hope levels were associated with reduced hope index scores (s-ATHS). On the whole, the present study demonstrates that AI-predicted hope levels are associated with hope index scores (s-ATHS), suggesting that monitoring AI-predicted hope levels may provide valuable insight in the practice of oncology.

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