DermaDashboard: Bridging the Gap Between FHIR Standards and Clinical Usability
Bibliographic record
Abstract
Unlabelled: Over the past decade, Fast Healthcare Interoperability Resources (FHIR) have become increasingly relevant in health care data standardization. However, the complex structure of FHIR makes cohort analytics with many-to-many relations extremely time-consuming, and, impossible in many cases. To support exploratory cohort building and data visualization in oncology, especially for nontechnical users, we developed the DermaDashboard, an interactive dashboard built on top of a relational FHIR-compliant PostgreSQL database. Relevant oncology data was preaggregated with a materialized view, and the subsequent visualization layer was implemented using an open-source visualization tool, enabling clinicians to filter and analyze data without requiring familiarity with FHIR or SQL. The database encompassed data from 3949 patients with melanoma and included 82,783 health records. Core FHIR resources were Patient, DiagnosticReport, and QuestionnaireResponse, with 54 mapped attributes spanning demographics, stagings, mutations, and treatments. The resulting dashboard allowed filtering across 29 variables to construct subcohorts and generate aggregation analyses. This implementation shows how open interoperability data standards, such as FHIR, can be used in the development of modular, user-friendly clinical dashboards for cohort analysis, and the architecture demonstrates a feasible path toward democratizing access to structured health care data.
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.
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 | 0.000 |
| 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".