Icon Art Design Language Combined with Real-time Intelligent Image Processing under Internet of Things

Gao, Yang

doi:10.1145/3522574

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…The second dataset (thyme) includes colorectal clinical notes of the THYME (Temporal History of Your Medical Events) corpus of Mayo Clinic data [15]. The third dataset (progress) consists of MIMIC-III progress notes derived from providers across different specialty intensive care units [16], [17]. We created classification instances for each dataset by extracting sections from all the notes.…”

Section: Datasetsmentioning

confidence: 99%

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Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining

Zhou

Yetisgen

Afshar

et al. 2023

Preprint

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Objective: The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for one institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP ("Subjective", "Object", "Assessment" and "Plan") framework with improved transferability. Materials and methods: We trained the baseline models by fine-tuning BERT-based models, and enhanced their transferability with continued pretraining, including domain adaptive pretraining (DAPT) and task adaptive pretraining (TAPT). We added out-of-domain annotated samples during fine-tuning and observed model performance over a varying number of annotated sample size. Finally, we quantified the impact of continued pretraining in equivalence of the number of in-domain annotated samples added. Results: We found continued pretraining improved models only when combined with in-domain annotated samples, improving the F1 score from 0.756 to 0.808, averaged across three datasets. This improvement was equivalent to adding 50.2 in-domain annotated samples. Discussion: Although considered a straightforward task when performing in-domain, section classification is still a considerably difficult task when performing cross-domain, even using highly sophisticated neural network-based methods. Conclusion: Continued pretraining improved model transferability for cross-domain clinical note section classification in the presence of a small amount of in-domain labeled samples.

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Section: Datasetsmentioning

confidence: 99%

“…When splitting the dataset into training and test set, for discharge, we randomly split the dataset with a 0.8/0.2 ratio. For thyme and progress, we followed the original train/test splits [15], [17].…”

Section: Datasetsmentioning

confidence: 99%