ClarusC64/long-covid-closed-loop-recovery-control-v1.0
What this dataset does This dataset tests whether a model can identify successful closed-loop recovery control in a synthetic Long Covid recovery setting. The task is not diagnosis. The task is control success prediction. Core stability idea A recovery plan may begin well but fail if feedback is poor, timing is wrong, adaptation is slow, or the control sequence diverges. This dataset tests whether models can identify when a recovery control loop is likely to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/long-covid-closed-loop-recovery-control-v1.0.
What this dataset does
This dataset tests whether a model can identify successful closed-loop recovery control in a synthetic Long Covid recovery setting.
The task is not diagnosis.
The task is control success prediction.
Core stability idea
A recovery plan may begin well but fail if feedback is poor, timing is wrong, adaptation is slow, or the control sequence diverges.
This dataset tests whether models can identify when a recovery control loop is likely to stabilize the system.
Prediction target
The target column is:
successful_recovery_control
Labels:
0 = failed control
1 = successful control
Row structure
Each row represents a synthetic Long Covid recovery control state.
Columns:
scenario_id
control_sequence_alignment_score
control_horizon
feedback_response_score
intervention_timing_score
adaptation_latency
control_stability_margin
sequence_divergence_margin
controller_confidence
recovery_consistency_score
control_recalibration_count
terminal_pathway_state
successful_recovery_control
Files
data/train.csv
data/test.csv
scorer.py
README.md
Evaluation
Predictions should use this format:
scenario_id,prediction
LC101,1
LC102,0
Run:
python scorer.py predictions.csv data/test.csv
The scorer reports:
accuracy
precision
recall_correct_control_success
f1
false_effective_control_rate
confusion_matrix
Primary metric:
recall_correct_control_success
Secondary metric:
false_effective_control_rate
Structural Note
This dataset is part of the Clarus / SIOS synthetic benchmark series.
It extends intervention competition into closed-loop recovery control.
The benchmark evaluates whether models can distinguish a promising intervention from a stabilizing control sequence.
Production Deployment
This dataset is synthetic.
It should not be used for clinical decision-making.
A production version would require longitudinal patient-level data, intervention sequences, feedback signals, and independently validated recovery outcomes.
Enterprise & Research Collaboration
Future versions may incorporate:
patient-level recovery time series
intervention timing
relapse events
autonomic feedback
immune profiling
metabolomics
symptom trajectories
control adaptation logs
treatment response data
License
MIT