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Graph-based Fusion Modeling and Explanation for Disease Trajectory Prediction
Amara Tariq1, Siyi Tang2, Hifza Sakhi3
1Department of Radiology, Mayo Clinic, AZ.
Medrxiv : the Preprint Server for Health Sciences
|November 3, 2022
Summary
This study introduces a novel relational graph for predicting clinical events in COVID-19 patients by integrating chest X-rays and electronic health records. The model identifies clinically similar patients to improve personalized predictions.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Computational Biology
Background:
- Personalized prediction of clinical events is crucial for managing complex diseases like COVID-19.
- Existing methods often rely on limited patient data or manual feature engineering.
- Integrating diverse data sources can enhance predictive accuracy for patient outcomes.
Conclusions:
- A novel relational graph approach enhances personalized clinical event prediction for COVID-19 patients.
- Integrating imaging and EHR data through a graph structure captures complex clinical patterns.
- The model's ability to learn from clinically similar patients and adapt to new cohorts offers a promising direction for precision medicine.
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