Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare (2105.07542v1)

Abstract: Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep learning based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured text. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention regulation strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.

An Analytical Overview of Collaborative Graph Learning in Healthcare Event Prediction

The paper "Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare" introduces a model aimed at enhancing the predictability and interpretability of health events through electronic health records (EHR). This model innovatively merges structured healthcare data (i.e., patient-disease interactions) and unstructured textual data (i.e., clinical notes) using a collaborative graph learning approach.

Graph Learning for Healthcare

The core of the proposed system is a collaborative graph learning model that addresses three intertwined challenges in extracting and utilizing data from EHR: domain knowledge utilization, collaborative patient-disease representation, and integration of auxiliary textual data. The model effectively combines three distinctive features:

1. Hierarchical Embedding of Diseases: The model employs ICD-9-CM disease codes and their hierarchical structure to understand relationships between different diseases. This embedding provides a knowledge-based representation crucial for enhancing the interpretation of patients’ health trajectories.
2. Dual Graph Structure: The collaborative graph incorporates a patient-disease observation graph and a disease ontology graph. The former captures real-world interactions between patients and diseases, while the latter utilizes hierarchical relationships among diseases to derive horizontal disease interactions. This dual graph approach allows for a more comprehensive learning architecture that integrates cross-categorical relationships among medical conditions.
3. Incorporation of Unstructured Text: The model includes a novel TF-IDF-rectified attention mechanism that guides the inclusion of clinical notes into the overall prediction process. This component allows the model to leverage potentially valuable context from narrative clinical documentation, thereby enriching the representation of each patient case.

Numerical Results

Experimentation on the MIMIC-III dataset demonstrated the practical utility of the proposed model. The model achieved superior performance compared to existing methods, including state-of-the-art RNN, CNN, and graph-based models. It delivered an improved weighted $F_1$ score of 22.97% and excellent recall rates (R@20 of 38.19% for diagnosis predictions), indicating a robust ability to predict both previously occurring and new-onset diseases. The model also performed strongly in heart failure prediction tasks, with an AUC of 85.66%, underscoring its general efficacy across different types of predictive tasks.

Implications and Future Directions

The proposed model's strengths lie in its integration of multiple types of healthcare data via an innovative graph-based learning platform, offering potential enhancements in personalized medicine and proactive patient care. By better capturing biomedical relationships and intelligently involving domain-specific data, the model sets a precedent for AI-driven health informatics approaches.

The promising results suggest several avenues for future inquiry and development. These include refining methods to further quantify the contributions of specific health events or admissions to a patient's overall health risk profile, potentially leading to more tailored interventions. Additionally, exploring the application of this framework to single admission records could expand its utility to various clinical scenarios, encompassing acute and chronic patient management contexts.

Conclusion

The intersection of graph learning and temporal prediction in this research presents an impactful advancement in healthcare informatics. The proposed system not only underscores the viability of integrating structured and unstructured data but also emphasizes the capacity of graph-based learning models to navigate complex healthcare datasets. Such innovations enhance our ability to predict patient outcomes and tailor healthcare services more effectively, suggesting a significant move forward in the application of AI in clinical settings.