FluxFae/Synthetic_Clinical_Telemetry

Synthetic Clinical Telemetry for Early-Warning Scoring

FluxFae/Synthetic_Clinical_Telemetry · dataset version 1.0.0 · generated 2026-07-01T00:00:00Z

A small, fully synthetic telemetry dataset for practising early-warning scoring (NEWS2) on wearable patient vitals. Every row is produced by a deterministic generator (seed=42): no real patients, no PHI, no scraped data, no value sampled from a clinical source.

What this dataset is not

• No real patients. No record here describes a real person.
• Not for clinical use. Nothing here is a medical device, a validated risk model, or clinical guidance. Do not deploy any model trained on it.
• Not an annotation set. The labels are computed from the generator's own scoring rules, so a model trained here learns those rules, not clinical judgement.

Tables

table	rows	columns	file
patients	500	5	patients.csv
telemetry_vitals	6000	22	telemetry_vitals.csv
telemetry_edge_cases	50	11	telemetry_edge_cases.csv

Each table is published twice: the Parquet file at the repository root (what the viewer and load_dataset read) and the generator's original artifact under data/, byte-identical to the closed export. Both carry the same values.

patients holds one row per patient and a split column with the frozen train / validation / test assignment (assigned per patient, so no patient crosses splits). telemetry_vitals holds the readings, joined to a patient by patient_id; the split of a reading is the split of its patient. telemetry_edge_cases holds deliberately malformed readings — out-of-range values and one row with every vital missing — for a validator to catch; it is a test fixture, not training data.

How to load it

from datasets import load_dataset

vitals = load_dataset("FluxFae/Synthetic_Clinical_Telemetry", "telemetry_vitals", split="train")
print(vitals.features)
print(vitals[0])

patients = load_dataset("FluxFae/Synthetic_Clinical_Telemetry", "patients", split="train")
edge_cases = load_dataset("FluxFae/Synthetic_Clinical_Telemetry", "telemetry_edge_cases", split="train")

Every config declares a single train split; the logical train / validation / test assignment travels as data in the split column, so a resampling step of your own stays explicit:

train = patients.filter(lambda row: row["split"] == "train")

Raw artifacts, for consumers that want the files exactly as exported:

from huggingface_hub import hf_hub_download

patients_csv = hf_hub_download("FluxFae/Synthetic_Clinical_Telemetry", "data/patients.csv", repo_type="dataset")

Read the dictionary first

data/data_dictionary.md documents every column: type, role (id, time, feature, target, protected, metadata, split), range, allowed values and derivation. Read it before using any column. Several columns are targets (news2_score_current, future_max_score_6h, deterioration_risk_6h, critical_risk_6h) and must never be used as model inputs, and at least one column is a protected attribute intended only for fairness checks.

Provenance

field	value
dataset version	1.0.0
seed	42
generation timestamp	2026-07-01T00:00:00Z (fixed, never the wall clock)
determinism	two independent runs produce byte-identical files

release_manifest.json at the repository root records, for every file this release publishes, its size and sha256, plus the sha256 of the source export's own manifest. Verify a download against it before trusting a value.

This repository publishes the telemetry tables only. The v1.0.0 export also contains a 500-row operator-ticket corpus; it is not part of this dataset, so data/validation_report.json describes the full export, including the two checks that mention that ticket table. Nothing else in the report is affected: the fourteen checks describe the telemetry data published here, and all of them pass.

Intended use

• Practising early-warning-score pipelines and deterioration-label logic on tabular telemetry.
• Benchmarking data validators, schema checks and reproducibility tooling against the edge-case table.
• Teaching time-series feature engineering under a strict no-leakage contract (label windows are declared per row).

Out of scope: clinical decision support, publication of clinical findings, any claim about real patient populations, and any fairness conclusion about real people.

License

Released under CC BY 4.0. You may use, share and adapt the data, including commercially, as long as you give appropriate credit.

Citation

@dataset{creator_dataset_telemetry_v1.0.0,
  title  = {Synthetic Clinical Telemetry for Early-Warning Scoring},
  year   = {2026},
  note   = {Synthetic dataset, CC BY 4.0; telemetry subset of Creator Dataset v1.0.0},
  url    = {https://huggingface.co/datasets/FluxFae/Synthetic_Clinical_Telemetry}
}