Continually-Adaptive Representation Learning Framework for Time-Sensitive Healthcare Applications.

Area of Science:

• Machine Learning
• Healthcare Informatics
• Artificial Intelligence

Background:

• Non-stationary environments pose challenges for machine learning models.
• Continual learning allows models to adapt to new data while retaining old knowledge.
• Healthcare data is dynamic, with entities like physicians and medications constantly changing.

Purpose of the Study:

• To propose a novel framework, Continually-Adaptive Representation Learning, for evolving healthcare applications.
• To adapt machine learning representations to changing data distributions.
• To address the under-exploration of continual learning in time-sensitive healthcare.

Main Methods:

• Exploiting healthcare entity context (e.g., interactions) to identify and retrain evolving representations.
• Leveraging patient information from clinical notes to generate robust healthcare embeddings.
• Developing a continual learning strategy for dynamic healthcare data.

Main Results:

• Demonstrated effectiveness of continually-adaptive representations in real-world healthcare datasets.
• Successful application in time-sensitive tasks like Clostridioides difficile Infection incidence prediction.
• Showcased utility in medical intensive care unit transfer prediction.

Conclusions:

• Continually-Adaptive Representation Learning effectively addresses non-stationary healthcare data.
• The framework offers practical benefits, especially in low-resource clinical settings.
• Enables robust and accurate healthcare embeddings by utilizing clinical notes.