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Poster
in
Workshop: 4th Workshop on Self-Supervised Learning: Theory and Practice

Language Model Training Paradigms for Clinical Feature Embeddings

Yurong Hu · Manuel Burger · Gunnar R├Ątsch · Rita Kuznetsova


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.

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