Artificial Intelligence System for Psychospiritual Distress in Family Caregivers of Patients With Terminal Cancer: A Retrospective Study.

Abstract

Family caregivers of patients with terminal cancer need psychospiritual care. The assessment of their psychospiritual distress is challenging. An automated system can be used to detect psychospiritual distress from large medical records in electronic medical records and help health care providers to accurately assess distress. This study aimed to develop an artificial intelligence system that automatically detects the psychological and spiritual distress of the families of patients with terminal cancer from unstructured text data in electronic medical records. This retrospective study collected medical records (n = 1,554,736) from 1 month before the participants died. The participants (n = 808) died at Tohoku University Hospital in Japan between January 1, 2018, and December 31, 2019. We randomly selected 10,000 records from physician and nursing records and split the data set into training and testing sets at a ratio of 70:30. We used the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) to evaluate the model performances. We used explain it like I am 5 and identified important expressions for detecting psychospiritual distress. The model with the highest performance for detecting psychological distress had AUROC and AUPRC values of 0.92 and 0.62, respectively. The model with the highest performance for detecting spiritual distress had values of 0.92 and 0.41, respectively. In psychological distress, the expressions with higher values were anxiety, worry, and tears. In spiritual distress, the expressions with higher values were want, me, and how. This study showed the application of machine learning models for the detection of psychospiritual distress among family caregivers of patients with terminal cancer from electronic medical records.