| Autor: |
Su, Xin, Miller, Timothy, Ding, Xiyu, Afshar, Majid, Dligach, Dmitriy |
| Rok vydání: |
2021 |
| Předmět: |
|
| DOI: |
10.48550/arxiv.2105.06752 |
| Popis: |
Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria. Phenotyping typically involves classifying long clinical documents that contain thousands of tokens. At the same time, recent state-of-art transformer-based pre-trained language models limit the input to a few hundred tokens (e.g. 512 tokens for BERT). We evaluate several strategies for incorporating pre-trained sentence encoders into document-level representations of clinical text, and find that hierarchical transformers without pre-training are competitive with task pre-trained models. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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