Developing Robust Clinical Text Deep Learning Models - A "Painless" Approach.

Area of Science:

• Clinical informatics
• Artificial intelligence in healthcare
• Natural language processing

Background:

• Deep learning models in healthcare require substantial labeled data, which is often scarce and costly to obtain.
• Data inadequacy poses challenges for developing robust and generalizable clinical text classifiers.
• Existing methods may struggle with performance on unseen data due to domain-specific limitations.

Purpose of the Study:

• To propose a generalized incremental multiphase framework for developing robust and performant deep learning classifiers for clinical text.
• To address the challenges of data inadequacy in the health domain for machine learning model development.
• To enhance the reliability and generalizability of clinical text analysis models.

Main Methods:

• The proposed framework incorporates incremental multiphases for assessing training data size.
• It includes a cross-validation setup designed to prevent test data bias.
• Robustness is evaluated through inter/intra-model significance analysis.

Main Results:

• The framework demonstrated effectiveness and generalizability in a real-world task.
• The study successfully identified patients presenting with 'pain' in the emergency department using the developed classifiers.
• The multiphase approach proved beneficial for optimizing model performance with limited data.

Conclusions:

• The generalized incremental multiphase framework offers a viable solution for building robust clinical text deep learning models.
• This approach mitigates issues related to data scarcity in the health domain.
• The framework's successful application in identifying emergency department pain cases highlights its practical utility.