A Survey on Publicly Available Open Datasets Derived From Electronic Health Records (EHRs) of Patients with Neuroblastoma
Bibliographic record
Abstract
<strong>Background:</strong> Neuroblastoma is a rare pediatric cancer that affects thousands of children worldwide. Information stored in electronic health records can be a useful source of data forin silicoscientific studies about this disease, carried out both by humans and by computational machines. Several open datasets derived from electronic health records of anonymized patients diagnosed with neuroblastoma are available in the internet, but they were released on different websites or as supplementary information of peer-reviewed scientific publications, making them difficult to find. <strong>Methods:</strong> To solve this problem, we present here this survey of five open public datasets derived from electronic health records of patients diagnosed with neuroblastoma, all collected in a single website called Neuroblastoma Electronic Health Records Open Data Repository. <strong>Results:</strong> The five open datasets presented in this survey can be used by researchers worldwide who want to carry on scientific studies on neuroblastoma, including machine learning and computational statistics analyses. <strong>Conclusions:</strong> We believe our survey and our open data resource can have a strong impact in oncology research, allowing new scientific discoveries that can improve our understanding of neuroblastoma and therefore improve the conditions of patients. We release the five open datasets reviewed here publicly and freely on our Neuroblastoma Electronic Health Records Open Data Repository under the CC BY 4.0 license at: <a href="https://davidechicco.github.io/neuroblastoma_EHRs_data" target="_blank">https://davidechicco.github.io/neuroblastoma_EHRs_data</a> or at <a href="https://doi.org/10.5281/zenodo.6915403" target="_blank">https://doi.org/10.5281/zenodo.6915403</a>
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".