Language Model Training Paradigms for Clinical Feature Embeddings

Yurong Hu; Manuel Burger; Gunnar Rätsch; Rita Kuznetsova

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arXiv · 2311.00768

Language Model Training Paradigms for Clinical Feature Embeddings

Yurong Hu·Manuel Burger·Gunnar Rätsch·Rita Kuznetsova

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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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Yurong Hu, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova. 2023-11-01. Language Model Training Paradigms for Clinical Feature Embeddings. https://arxiv.org/abs/2311.00768

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