metadata
language:
- en
license: mit
pretty_name: Clinical Personal Deviation Vector Detection v0.1
dataset_name: clinical-personal-deviation-vector-detection-v0.1
tags:
- clarusc64
- clinical
- n-of-1
- deviation
- manifold
- early-detection
task_categories:
- text-classification
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
- split: test
path: data/test.csv
What this dataset tests
Whether a model can detect deviation from a person's own coherent basin
using baseline envelope and coupling structure.
Required outputs
- deviation_vector
- deviation_severity_score_0_100
- first_system_departing
Deviation vector fields
- direction
- magnitude
- velocity
- coupling_loss
- onset_time
- cross_modal_consensus
First system labels
- sleep_circadian
- autonomic
- immune_inflammatory
- metabolic
- neurocognitive
- gut_microbiome
- behavior_load
- subjective_experience
Typical failures
- treating population thresholds as baseline
- outputting severity with no vector structure
- ignoring coupling changes
Suggested prompt wrapper
System
You detect deviation from a personal baseline.
User
Baseline signature
{baseline_signature}
Baseline envelope
{baseline_envelope}
Recent data
Sleep: {recent_data_sleep}
Wearables: {recent_data_wearables}
Labs: {recent_data_labs}
Behavior: {recent_data_behavior}
Subjective: {recent_data_subjective}
Coupling changes
{coupling_changes}
Return
- deviation vector with required fields
- severity score
- first system departing
- one sentence evidence
Citation
ClarusC64 dataset family