Language Model Training Paradigms for Clinical Feature Embeddings

• URL: http://arxiv.org/abs/2311.00768v2
• Date: Tue, 6 Feb 2024 16:33:48 GMT
• Title: Language Model Training Paradigms for Clinical Feature Embeddings
• Authors: Yurong Hu, Manuel Burger, Gunnar R\"atsch, Rita Kuznetsova
• Abstract summary: We use self-supervised training paradigms for language models to learn high-quality clinical feature embeddings. We visualize the learnt embeddings via unsupervised dimension reduction techniques and observe a high degree of consistency with prior clinical knowledge.
• Score: 1.4513150969598638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In research areas with scarce data, representation learning plays a significant role. This work aims to enhance representation learning for clinical time series by deriving universal embeddings for clinical features, such as heart rate and blood pressure. We use self-supervised training paradigms for language models to learn high-quality clinical feature embeddings, achieving a finer granularity than existing time-step and patient-level representation learning. We visualize the learnt embeddings via unsupervised dimension reduction techniques and observe a high degree of consistency with prior clinical knowledge. We also evaluate the model performance on the MIMIC-III benchmark and demonstrate the effectiveness of using clinical feature embeddings. We publish our code online for replication.

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