Graph-based machine learning algorithms for predicting disease outcomes
Why this work is in the frame
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Bibliographic record
Abstract
Improving disease outcome prediction can greatly aid in the strategic deployment of secondary prevention approaches.We develop two methods to predict the evolution of diseases by taking into account personal attributes of the subjects and their relationships with medical examination results.Our approaches build upon a recent formulation of this problem as a graph-based geometric matrix completion task.The primary innovation is the introduction of multiple graphs, each relying on a different combination of subject attributes.Via statistical significance tests, we determine the relevant graph(s) for each medically-derived feature.In the first approach, we then employ a multiple-graph recurrent graph convolutional neural network architecture to predict the disease outcomes.In the second approach, we use a multiple-graph graph auto-encoder architecture to predict the disease outcomes.We demonstrate the efficacy of the two techniques by addressing the task of predicting the development of Alzheimer's disease for patients exhibiting mild cognitive impairment, showing that the incorporation of multiple graphs improves predictive capability.Moreover, in the second approach, the use of a graph autoencoder also helps in increasing predictive capability.I would like to thank Florence Robert-Regol (Master's student) and Soumyasundar Pal (PhD candidate) for the numerous discussions that we had on many interesting topics such as those on graph convolutional neural networks or other discussions related to graph signal processing and machine learning techniques for graph-structured data.I would also like to thank them for their help while I was having issues with my work, both theoretically and during the implementation step.I would also like to thank Laure Abecassis for her feedback when I was writing my thesis.I would like to thank all the members of the Computer Networks Lab for providing an inviting and enriching environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it