| --- |
| language: en |
| license: mit |
| task_categories: |
| - tabular-classification |
| tags: |
| - long-covid |
| - multi-omics |
| - recovery |
| - systems-biology |
| - sios |
| - interaction-geometry |
| size_categories: |
| - n<1K |
| pretty_name: Long Covid Recovery Interaction Geometry |
| --- |
| |
| # What this dataset does |
|
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| This dataset tests whether failed Long Covid recovery is better predicted by interaction geometry than by any single biological burden signal. |
|
|
| The target is not disease diagnosis. |
|
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| The target is failed recovery. |
|
|
| # Core stability idea |
|
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| A patient may show high biological burden and still recover if repair capacity remains strong and omics layers stay coherent. |
|
|
| A patient may show moderate burden and fail to recover if repair capacity weakens and biological layers begin to diverge. |
|
|
| The central hypothesis is: |
|
|
| ```text |
| failed recovery = burden × disagreement × weak repair |
| |
| Prediction target |
| label = 1 |
| |
| means failed or stuck recovery trajectory. |
| |
| label = 0 |
| |
| means recovered or stable recovery trajectory. |
| |
| Row structure |
| |
| Each row represents a synthetic patient recovery state. |
| |
| Columns: |
| |
| scenario_id |
| ifn_signature |
| cytokine_activation |
| acute_phase_proteins |
| mitochondrial_stress |
| lipid_disruption |
| microbiome_instability |
| oxidative_stress |
| repair_response |
| symptom_score |
| label |
| Biological interpretation |
| |
| The dataset is biology-shaped but synthetic. |
| |
| It is designed to test whether models can detect interaction structure across immune, metabolic, microbiome, oxidative, and repair signals. |
| |
| It does not claim patient-level validation. |
| |
| Files |
| data/train.csv |
| data/test.csv |
| scorer.py |
| README.md |
| Evaluation |
| |
| Submit predictions as: |
| |
| scenario_id,prediction |
| LC101,0 |
| LC102,0 |
| LC103,1 |
| |
| Run: |
| |
| python scorer.py predictions.csv data/test.csv |
| |
| The scorer reports: |
| |
| accuracy |
| precision |
| recall |
| f1 |
| confusion matrix |
| Structural Note |
| |
| This dataset is part of the Clarus / SIOS synthetic benchmark series. |
| |
| It is designed to test whether models can identify latent stability geometry rather than rely on single-variable correlation. |
| |
| The hidden structure is not intended to represent a clinical rule. |
| |
| The purpose is to evaluate whether a model can reason across interacting biological signals. |
| |
| Production Deployment |
| |
| This dataset should not be used for clinical decision-making. |
| |
| A production version would require validated patient-level Long Covid multi-omics data, longitudinal outcomes, and independent external testing. |
| |
| Enterprise & Research Collaboration |
| |
| This benchmark can be extended into real-data research using public or partner datasets containing transcriptomics, proteomics, metabolomics, immune profiling, symptom trajectories, and recovery outcomes. |
| |
| License |
| |
| MIT |
