Key result
DeepNote-GNN outperforms baselines for 30-day readmission prediction by integrating clinical notes and patient network structure.
Why the study?
Most clinical predictive models rely on structured EHR data, while unstructured clinical notes and patient networks remain underused for predicting clinical outcomes.
A novel hybrid deep learning model, DeepNote-GNN, integrating clinical notes and patient network data, improves 30-day hospital readmission prediction compared to baseline models.
Answers are grounded in this paper and cite their sources.
May enhance readmission risk stratification; leaves open prospective validation before clinical adoption.
With the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task.
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Golmaei et al. (2021) studied Hospital readmission. DeepNote-GNN vs. State-of-the-art baselines was evaluated on 30-day hospital readmission. DeepNote-GNN, a hybrid deep learning model integrating clinical notes and patient network structure, achieved superior performance compared to baselines for 30-day hospital readmission prediction.
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