Graph Space Embedding
This work addresses interpretability and efficiency in clinical prediction models, though it appears incremental as it builds on existing embedding methods.
The authors tackled the problem of modeling interactions in data with a Graph Space Embedding (GSE) technique, achieving far better performance than traditional algorithms on a real-world clinical cohort for coronary artery disease.
We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide theoretical results on an optimal regime for the GSE, namely a feasibility region for its parameters, and demonstrate the experimental relevance of our findings. Next, we introduce a strategy to gain insight on which interactions are responsible for the certain predictions, paving the way for a far more transparent model. In an empirical evaluation on a real-world clinical cohort containing patients with suspected coronary artery disease, the GSE achieves far better performance than traditional algorithms.