Predicting health-related quality of life change using natural language processing in thyroid cancer.

ABSTRACT:

Background

Patient-reported outcomes (PRO) allow clinicians to measure health-related quality of life (HRQOL) and understand patients' treatment priorities, but obtaining PRO requires surveys which are not part of routine care. We aimed to develop a preliminary natural language processing (NLP) pipeline to extract HRQOL trajectory based on deep learning models using patient language.

Materials and methods

Our data consisted of transcribed interviews of 100 patients undergoing surgical intervention for low-risk thyroid cancer, paired with HRQOL assessments completed during the same visits. Our outcome measure was HRQOL trajectory measured by the SF-12 physical and mental component scores (PCS and MCS), and average THYCA-QoL score.We constructed an NLP pipeline based on BERT,

SUBMITTER: Lian R 

PROVIDER: S-EPMC10473865 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature