Visual Representation of Tabular Electronic Health Records for Predicting Sudden Cardiac Arrest
Bibliographic record
Abstract
Computer-aided diagnosis in healthcare involves collecting and modeling patients' comprehensive health-related information from their Electronic Health Records (EHRs). Recently, the extensive acquisition of EHRs and scalable architectures of deep learning methods such as convolutional neural networks (CNNs) are generating robust healthcare assistance. Yet, the laborious pre-processing steps on EHR data and domain-specific feature engineering hinder the results of the model's interpretability and transparency. Moreover, conducting obscure feature engineering tasks without domain knowledge may lead to the loss of relevant features, limiting the model's ability to make reliable predictions. To alleviate these challenges, we proposes a method to represent the tabular EHR data in 2D images without leveraging any pre-processing or data cleaning tasks, which results in a generalized and more interpretable visualization of EHR tabular data. In addition, we evaluate the proposed method by predicting a cardiovascular disease, Sudden Cardiac Arrest (SCA), using pre-trained deep CNN models. The results demonstrate that the proposed method can perform highly in the SCA prediction without any missing value imputation and allow more transparency with less human expert intervention.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".