We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Advanced Sampling Technique in Radiology Free-Text Data for Efficiently Building Text Mining Models by Deep Learning in Vertebral Fracture.
- Authors
Hung, Wei-Chieh; Lin, Yih-Lon; Lin, Chi-Wei; Chin, Wei-Leng; Wu, Chih-Hsing
- Abstract
This study aims to establish advanced sampling methods in free-text data for efficiently building semantic text mining models using deep learning, such as identifying vertebral compression fracture (VCF) in radiology reports. We enrolled a total of 27,401 radiology free-text reports of X-ray examinations of the spine. The predictive effects were compared between text mining models built using supervised long short-term memory networks, independently derived by four sampling methods: vector sum minimization, vector sum maximization, stratified, and simple random sampling, using four fixed percentages. The drawn samples were applied to the training set, and the remaining samples were used to validate each group using different sampling methods and ratios. The predictive accuracy was measured using the area under the receiver operating characteristics (AUROC) to identify VCF. At the sampling ratios of 1/10, 1/20, 1/30, and 1/40, the highest AUROC was revealed in the sampling methods of vector sum minimization as confidence intervals of 0.981 (95%CIs: 0.980–0.983)/0.963 (95%CIs: 0.961–0.965)/0.907 (95%CIs: 0.904–0.911)/0.895 (95%CIs: 0.891–0.899), respectively. The lowest AUROC was demonstrated in the vector sum maximization. This study proposes an advanced sampling method, vector sum minimization, in free-text data that can be efficiently applied to build the text mining models by smartly drawing a small amount of critical representative samples.
- Subjects
TEXT mining; VERTEBRAL fractures; DEEP learning; SAMPLING (Process); RECEIVER operating characteristic curves
- Publication
Diagnostics (2075-4418), 2024, Vol 14, Issue 2, p137
- ISSN
2075-4418
- Publication type
Article
- DOI
10.3390/diagnostics14020137