Carefully Optimized and Rigorously Evaluated BEHRT: Insights into Improving Transformer-based Models for Electronic Health Records

核心概念

Careful optimization and rigorous evaluation of the BEHRT model can significantly improve its performance on a diverse set of clinical prediction tasks, providing insights into effective data representation and model architecture choices for transformer-based EHR analysis.

摘要

The paper introduces CORE-BEHRT, a carefully optimized and rigorously evaluated version of the BEHRT model for Electronic Health Record (EHR) analysis. The authors conduct a systematic optimization process, examining the impact of various data representation and technical components on the model's performance across three generic downstream tasks: death prediction, pain treatment prediction, and general infection prediction.
Key insights from the optimization experiments:

Data Representation:

Inclusion of medication codes and timestamps significantly improved performance, increasing the average AUROC from 0.785 to 0.797 (p < 10^-7).
Using full-depth ICD-10 and ATC codes, as well as including patient sex, provided smaller but still meaningful improvements.
Removing separator tokens worsened performance, while simplified segment embeddings yielded improvements.

Technical Components:

Incorporating Time2Vec embeddings for age and the improved transformer recipe (RoPE and SwiGLU activation) provided additional performance gains, increasing the average AUROC to 0.801 (p < 10^-7).
Experiments with different masking ratios during pre-training showed that a 20% masking ratio was optimal, but the impact was inconsistent across tasks.
Bidirectional Gated Recurrent Units (BiGRU) outperformed other pooling strategies for fine-tuning.

The authors then conducted a rigorous evaluation of the optimized CORE-BEHRT model across 25 diverse clinical prediction tasks, covering a wide range of health conditions. The results showed significant performance improvements in 17 out of 25 tasks and improvements in 24 tasks, highlighting the generalizability of the optimized model.
The study provides a strong foundation for future work on transformer-based EHR models, offering insights into effective data representation and model architecture choices. The authors emphasize the importance of careful optimization and rigorous evaluation to increase the trustworthiness of these models for potential clinical adoption.

統計資料

Inclusion of medication codes increased average sequence length by 143.3 codes and training time by 5 times compared to baseline.
Using full-depth ICD-10 and ATC codes increased the model size by 3.7 times up to 7.933 million parameters.

引述

"Careful optimization and rigorous evaluation of the BEHRT model can significantly improve its performance on a diverse set of clinical prediction tasks, providing insights into effective data representation and model architecture choices for transformer-based EHR analysis."
"Improving data representation consistently, except for placental insufficiency, improves performance. However, expanding the data representation can be associated with increased model size and training times."
"The technical components give the models an additional performance boost across all but four tasks and reach significance in seven of them. The technical components also never perform significantly worse and do not affect training times much, making them a solid addition to the optimized model."

從以下內容提煉的關鍵洞見

CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT

by Mikk... 於 arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15201.pdf

深入探究

How can the pre-training objective be further tailored to better align with the downstream EHR tasks, beyond the masked language modeling and prolonged length of stay prediction used in this study

To better align the pre-training objective with downstream EHR tasks, beyond the traditional masked language modeling (MLM) and prolonged length of stay prediction, several tailored approaches can be considered. One approach could involve incorporating task-specific pre-training objectives that directly relate to the clinical prediction tasks. For example, introducing auxiliary tasks during pre-training that simulate specific clinical scenarios or conditions relevant to the downstream tasks could enhance the model's ability to capture domain-specific patterns. These auxiliary tasks could include predicting disease progression, treatment response, or comorbidity patterns based on the EHR data.
Another strategy could involve designing pre-training tasks that focus on capturing temporal dependencies and long-range interactions within the EHR data. Since EHR data often exhibit complex temporal dynamics, incorporating pre-training tasks that emphasize temporal reasoning, such as predicting the sequence of events or patient trajectories, could improve the model's understanding of patient histories and outcomes.
Furthermore, leveraging self-supervised learning techniques that encourage the model to learn meaningful representations from unlabeled data could be beneficial. For instance, self-supervised tasks like contrastive learning, where the model learns to differentiate between similar and dissimilar patient sequences, could help the model capture subtle patterns and relationships in the data that are crucial for accurate clinical predictions.
Overall, by tailoring the pre-training objectives to focus on specific aspects of EHR data relevant to the downstream tasks, the model can better learn the intricate patterns and dependencies present in the data, leading to improved performance on clinical prediction tasks.

What other data sources, such as lab tests, vital signs, and procedures, could be effectively integrated into the model, and how would that impact performance and model complexity

Integrating additional data sources such as lab tests, vital signs, and procedures into the model can provide valuable insights and enhance the model's predictive capabilities. Including lab tests can offer crucial information about a patient's physiological state, biomarkers, and disease markers, enabling the model to capture detailed health profiles and disease progression. Vital signs data, including parameters like blood pressure, heart rate, and temperature, can provide real-time indicators of a patient's health status, aiding in early detection and monitoring of conditions.
Incorporating procedure codes into the model can help capture treatment pathways, interventions, and surgical histories, providing a comprehensive view of a patient's healthcare journey. By integrating these diverse data sources, the model can leverage a richer set of features to make more accurate predictions and recommendations.
However, the integration of additional data sources may also introduce challenges related to data preprocessing, feature engineering, and model complexity. Managing the increased dimensionality of the input data, addressing missing values, and ensuring the compatibility of different data modalities are essential considerations. Advanced techniques such as multi-modal fusion, attention mechanisms, and feature selection methods can help effectively integrate and leverage diverse data sources while managing model complexity.
While the inclusion of more data sources may enhance the model's performance, careful consideration of the trade-offs between increased information richness and model complexity is crucial to maintain a balance that optimizes predictive accuracy and computational efficiency.

Given the observed performance saturation for certain tasks, how can the model architecture and training process be further optimized to continue improving performance without relying solely on increased data size

To address performance saturation for certain tasks without solely relying on increased data size, further optimization of the model architecture and training process is essential. One approach is to explore more sophisticated model architectures that can capture complex relationships and patterns in the data more effectively. For example, incorporating attention mechanisms, recurrent neural networks (RNNs), or transformer variants with specialized modules for handling temporal data could improve the model's ability to learn from sequential EHR data.
Additionally, optimizing the training process by fine-tuning hyperparameters, regularization techniques, and learning rate schedules can help prevent overfitting and enhance generalization performance. Techniques such as curriculum learning, where the model is trained on progressively more challenging examples, and transfer learning, leveraging pre-trained models on related tasks, can also boost performance without solely relying on data size.
Furthermore, exploring ensemble methods that combine predictions from multiple models or incorporating domain-specific knowledge through expert rules or domain-specific embeddings can enhance the model's predictive power. By leveraging a combination of advanced architectures, optimized training strategies, and ensemble techniques, the model can continue to improve performance on challenging tasks while mitigating the effects of performance saturation.