GRAM: Graph-based Attention Model for Healthcare Representation Learning (1611.07012v3)

Abstract: Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose a GRaph-based Attention Model, GRAM that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.

• The paper presents GRAM, a model using hierarchical ontologies to improve healthcare data representation despite data insufficiency.
• The paper shows a 10% accuracy boost for rare diseases and a 3% AUC increase in heart failure prediction compared to standard RNNs.
• The paper enhances interpretability by using a soft attention mechanism that aligns learned embeddings with established clinical concepts.

Overview of GRAM: Graph-based Attention Model for Healthcare Representation Learning

The paper, "GRAM: Graph-based Attention Model for Healthcare Representation Learning," investigates the challenges of data insufficiency and interpretability in healthcare predictive modeling. It introduces GRAM, a novel approach leveraging hierarchical medical ontologies to enhance deep learning models' performance, specifically in handling electronic health records (EHR).

Key Contributions

The authors address two principal challenges:

1. Data Insufficiency: Deep learning models often demand large datasets, which can be a limitation in healthcare where specific diseases may be underrepresented.
2. Interpretability: The learned representations need to align with established medical knowledge for effective application.

GRAM integrates hierarchical information from medical ontologies, such as the ICD, CCS, and SNOMED-CT, using an attention mechanism. This approach enables the model to represent medical concepts as a combination of their ancestors, thus effectively utilizing limited data.

Methodology

GRAM assigns each medical concept in a knowledge DAG an embedding based on its occurrences and its ancestors' contributions. The model dynamically adjusts the representation of each concept via a soft attention mechanism, enhancing the robustness and interpretability of the learned representations. This mechanism is trained jointly with a recurrent neural network aimed at predicting disease outcomes.

Experimental Evaluation

The paper presents comprehensive experiments on three prediction tasks using real-world datasets—Sutter PAMF, MIMIC-III, and a Sutter heart failure cohort. The results show that GRAM achieves superior predictive performance, particularly in scenarios with significant data insufficiency. Notably, GRAM demonstrates:

• A 10% improvement in accuracy for rare diseases compared to baseline RNN models.
• A 3% increase in AUC for heart failure prediction, utilizing significantly less training data.

Interpretability and Attention Behavior

The interpretability of GRAM is highlighted through qualitative evaluations of the learned representations. The paper demonstrates that GRAM clusters similar medical concepts effectively. Additionally, the attention behavior analysis shows intuitive alignment with existing medical knowledge, where more weight is given to common ancestors under data scarcity.

Implications and Future Directions

The results imply that GRAM could serve as a powerful tool in healthcare analytics, allowing models to perform reliably even with rare diseases or smaller datasets. The ability to utilize hierarchical medical ontologies meaningfully could spur further research into integrating structured medical knowledge into AI models. Future work may explore broader applications, such as integrating more complex ontologies or adapting the approach to other fields with hierarchical data.

In conclusion, GRAM presents a significant advancement in healthcare representation learning by effectively addressing data insufficiency and enhancing model interpretability, setting a foundation for future explorations in AI-driven healthcare solutions.