JMIR Publications

ABSTRACT

Background:

The diagnosis of sleep disorders presents a challenging landscape, characterized by the complex nature of their assessment and the often divergent views between objective clinical assessment and subjective patient experience. This study explores the interplay between these perspectives, focusing on the variability of individual perceptions of sleep quality and latency.

Objective:

Our primary goal is to investigate the alignment, or lack thereof, between subjective experiences and objective measures in the assessment of sleep disorders.

Methods:

To study this, we developed an aspect-based sentiment analysis method: Using large language models (Falcon 40b and Mixtral 8X7B), we are identifying entity groups of three aspects related to sleep behavior (day sleepiness, sleep quality, fatigue). From phrases referring to these aspects, we are assigning sentiment values between 0 and 1 using a BERT-BiLSTM-based approach (accuracy 78%) and a fine-tuned GPT-2 sentiment classifier (accuracy 87%).

Results:

The results show that our approach is able to handle the specialized language occurring in the sleep disorder domain and identify the sentiment and opinion in clinical records.

Conclusions:

Our method has potential in uncovering critical insights into patient self-perception versus clinical evaluations. Clinical Trial: The secondary usage of Berner Sleep Data Base (BSDB) from Inselspital, University Hospital Bern, was approved by the local ethics committee (KEK-Nr. 2022-00415)