Health information network representation learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapidly growing volume of Electronic Health Records (EHR) data, deep-learning models exhibit state-of-the-art performance for many predictive tasks in the health domain.To overcome the challenge of high dimensionality in EHR data, many representation learning methods have been proposed to learn low-dimensional diagnosis representations.Another challenge is how to effectively incorporate the domain knowledge, such as the ICD medical ontology, into the learned embeddings.Albeit the medical ontology is a knowledge graph, none of the existing methods take advantage of Graph Neural Network (GNN), which has demonstrated its ability in other domains.The problem is that a GNN with multiple hidden layers, which are required to propagate information from the leaf of the medical ontology graph to the root, dilutes the differences among the nodes, degrading the quality of the learned embeddings.In this thesis, we introduce a densely connected graph derived from the original ontology graph to tackle His inspiration for the direction of the research and his tutoring on the logic flow, academic writing, notation, and grammar were indispensable to the completion of this paper.Second, I would like to thank Dr. Reihaneh Rabbany for her careful review and valuable suggestions, which have greatly helped me enhance this thesis.Also, I would like to
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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