Multimodal spatiotemporal graph neural networks for improved prediction
of 30-day all-cause hospital readmission
- URL: http://arxiv.org/abs/2204.06766v1
- Date: Thu, 14 Apr 2022 05:50:07 GMT
- Title: Multimodal spatiotemporal graph neural networks for improved prediction
of 30-day all-cause hospital readmission
- Authors: Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha
Elugunti, Daniel Rubin, Bhavik N. Patel, Imon Banerjee
- Abstract summary: We propose a multimodal, modality-agnostic graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission.
MM-STGNN achieves AU of 0.79 on both primary and external datasets.
For subset populations of patients with heart and vascular disease, our model also outperforms baselines on predicting 30-day readmission.
- Score: 4.609543591101764
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Measures to predict 30-day readmission are considered an important quality
factor for hospitals as accurate predictions can reduce the overall cost of
care by identifying high risk patients before they are discharged. While recent
deep learning-based studies have shown promising empirical results on
readmission prediction, several limitations exist that may hinder widespread
clinical utility, such as (a) only patients with certain conditions are
considered, (b) existing approaches do not leverage data temporality, (c)
individual admissions are assumed independent of each other, which is
unrealistic, (d) prior studies are usually limited to single source of data and
single center data. To address these limitations, we propose a multimodal,
modality-agnostic spatiotemporal graph neural network (MM-STGNN) for prediction
of 30-day all-cause hospital readmission that fuses multimodal in-patient
longitudinal data. By training and evaluating our methods using longitudinal
chest radiographs and electronic health records from two independent centers,
we demonstrate that MM-STGNN achieves AUROC of 0.79 on both primary and
external datasets. Furthermore, MM-STGNN significantly outperforms the current
clinical reference standard, LACE+ score (AUROC=0.61), on the primary dataset.
For subset populations of patients with heart and vascular disease, our model
also outperforms baselines on predicting 30-day readmission (e.g., 3.7 point
improvement in AUROC in patients with heart disease). Lastly, qualitative model
interpretability analysis indicates that while patients' primary diagnoses were
not explicitly used to train the model, node features crucial for model
prediction directly reflect patients' primary diagnoses. Importantly, our
MM-STGNN is agnostic to node feature modalities and could be utilized to
integrate multimodal data for triaging patients in various downstream resource
allocation tasks.
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