xpertsystems/hlt009-sample

HLT-009 — Synthetic Continuous Vital Sign Monitoring Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-009 commercial product from XpertSystems.ai.

A fully synthetic ICU continuous vital sign monitoring dataset combining 12-stream time-series vitals (HR/SpO2/RR/NBP/IBP/Temp/EtCO2/CVP/CO), alarm event logs with true/false labels, intervention logs (medication boluses, ventilator adjustments, code events), and 53-column episode-level summary data — calibrated to MIMIC-IV / eICU-CRD benchmarks with APACHE-II, SOFA, NEWS2, qSOFA, and CCI severity scoring.

⚠️ PRIVACY & SYNTHETIC NATURE Every record in this dataset is 100% synthetic. No real patient data, no PHI, no real medical device readings. Population-level distributions match published MIMIC-IV / eICU-CRD / Drew et al. benchmarks but the episodes and waveforms are computationally generated.

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What's in this sample

File	Rows	Cols	Description
vitals_timeseries.csv	~26,700	19	One row per episode-timestep (5-min resolution). 12 vital streams + NEWS2 + qSOFA + artifact flag + 6h rolling features
alarm_events.csv	~170	15	One row per alarm event. Type, priority (IEC 60601-1-8), true/false flag, false-alarm subtype, response time, override flag
interventions.csv	~15	8	One row per clinical intervention (medication bolus, ventilator adjustment, code event, rapid response)
episode_summary.csv	25	53	One row per episode. Demographics, APACHE-II, SOFA, CCI, ventilation/vasopressor/RRT flags, LOS, NEWS2 max/mean, deterioration label, mortality, 12 signal quality indices

Total: ~5.3 MB across 5 files.

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Schema highlights

vitals_timeseries.csv (19 columns, ~1,100 rows per episode at 5-min resolution)

Identity: episode_id, timestamp

12 vital streams (calibrated to MIMIC-IV physiological ranges):

• Cardiovascular: hr_bpm, nbp_sys_mmhg, nbp_dia_mmhg, nbp_map_mmhg, ibp_sys_mmhg, ibp_dia_mmhg, cvp_mmhg, cardiac_output_lpm
• Respiratory: spo2_pct, rr_bpm, etco2_mmhg
• Thermoregulation: temp_c

Derived & quality: artifact_flag (4% rate per timestep), news2_score (RCP NEWS2 computed at each step), qsofa_score (Sepsis-3 qSOFA), news2_roll_max_4h, news2_rate_of_rise

alarm_events.csv (15 columns)

alarm_id, episode_id, alarm_type (18 types: HIGH_HR, LOW_HR, CRITICAL_LOW_HR, LOW_SPO2, CRITICAL_LOW_SPO2, HIGH_RR, APNEA, HIGH_SBP, LOW_SBP, LOW_MAP, HIGH_ETCO2, LOW_ETCO2, HIGH_CVP, LOW_CVP, HIGH_CO, LOW_CO, HIGH_IBP_SYS, LOW_IBP_SYS), alarm_priority (IEC 60601-1-8: LOW/MEDIUM/HIGH/CRITICAL), alarm_onset_ts, alarm_duration_sec, true_alarm_flag, false_alarm_subtype (Artifact / Motion / LeadOff / TechnicalError), response_time_min, intervention_triggered, override_flag, limit_at_alarm_low, limit_at_alarm_high, alarm_cascade_id, shift (Day/Evening/Night)

interventions.csv (8 columns)

intervention_id, episode_id, intervention_type (MEDICATION_BOLUS / VENTILATOR_ADJUSTMENT / POSITION_CHANGE / PHYSICIAN_NOTIFICATION / RAPID_RESPONSE_ACTIVATION / CODE_EVENT / NURSING_ASSESSMENT), intervention_ts, triggered_by_alarm, time_from_alarm_min, clinician_role, intervention_outcome

episode_summary.csv (53 columns)

Identity & setting: episode_id, monitoring_setting (ICU), icu_unit_type (MICU/SICU/CCU/Neuro ICU), bed_id, admit_dt, discharge_dt, episode_duration_days

Demographics & severity: age, sex, apache2_score (Knaus 1985), sofa_score (Vincent 1996), sofa_at_discharge, cci_score (Charlson 1987), primary_dx_group (Sepsis/Respiratory Failure/Cardiac/Neuro/Post-Surgical/Trauma/Other), trajectory (Stable/Improving/Deteriorating/Oscillating)

Clinical interventions: ventilation_status, vasopressor_flag, rrt_flag (renal replacement therapy), has_arterial_line, has_central_line, has_pa_catheter

Device metadata: monitor_manufacturer (Philips IntelliVue MX800 / GE Carescape B850 / Masimo Root / Nihon Kohden BSM-6000), rpm_device_type, lead_configuration (3-lead / 5-lead / 12-lead), device_uptime_pct, connectivity_drops

Alarm fatigue metrics (Drew et al. 2014): true_alarm_rate, total_alarms, alarms_per_patient_day, actionable_alarm_rate, alarm_override_rate, median_response_time_min, alarm_limit_modification_count, alarm_cascade_count, fatigue_index_score

Early warning & outcomes: news2_max, news2_mean, qsofa_max, deterioration_6h_label, in_hospital_mortality, readmission_30d, rapid_response_event

Signal Quality Indices (SQI): 12 columns sqi_* — one per vital stream

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Calibration source story

The full HLT-009 generator anchors all distributions to authoritative critical care references:

• MIMIC-IV (Johnson et al. Scientific Data 2023) — ICU vital signs benchmark, LOS Weibull(1.4, 5.2), severity distributions
• eICU-CRD (Pollard et al. Scientific Data 2018) — Multi-center ICU database, ventilation/vasopressor rates
• APACHE-II (Knaus et al. Crit Care Med 1985) — Acute Physiology and Chronic Health Evaluation
• SOFA (Vincent et al. Intensive Care Med 1996) — Sequential Organ Failure Assessment
• NEWS2 (Royal College of Physicians 2017) — National Early Warning Score 2
• qSOFA (Singer et al. JAMA 2016) — Sepsis-3 Quick SOFA
• CCI (Charlson et al. J Chron Dis 1987) — Charlson Comorbidity Index
• Drew et al. (2014) PLoS ONE — Alarm fatigue benchmark (187 alarms/bed/day)
• Joint Commission Sentinel Event Alert 50 (2013) — Alarm safety standards
• Wunsch et al. (2010) JAMA — US ICU mechanical ventilation prevalence
• IEC 60601-1-8 — Medical electrical equipment alarm priority standard

Sample-scale validation scorecard

Metric	Observed	Target	Tolerance	Status	Source
Mean APACHE-II score	11.6	12.0	±4.0	✅ PASS	Knaus et al. (1985) / MIMIC-IV
Mean SOFA score	3.1	3.5	±2.0	✅ PASS	Vincent et al. (1996)
Median LOS (days)	2.99	4.0	±2.0	✅ PASS	MIMIC-IV (Johnson et al. 2023)
Ventilation rate	56%	40%	±20%	✅ PASS	Wunsch et al. (2010)
Mean NEWS2 score	4.29	4.0	±1.5	✅ PASS	RCP NEWS2 (2017)
True alarm rate	17.1%	20%	±10%	✅ PASS	Joint Commission SE Alert 50
Artifact flag rate	3.85%	4%	±2%	✅ PASS	Wong et al. (2018) ICU data quality
Vital stream count	12	12	—	✅ PASS	Schema coverage
Alarm priority diversity	2	≥2	—	✅ PASS	IEC 60601-1-8
Timeseries temporal monotonicity	100%	100%	—	✅ PASS	Data hygiene

Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).

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Loading examples

Pandas — explore the episode summary

import pandas as pd

summary = pd.read_csv("episode_summary.csv")
vitals = pd.read_csv("vitals_timeseries.csv", parse_dates=["timestamp"])
alarms = pd.read_csv("alarm_events.csv", parse_dates=["alarm_onset_ts"])

# Severity by primary diagnosis
print(summary.groupby("primary_dx_group")[
    ["apache2_score", "sofa_score", "episode_duration_days"]
].mean().round(2))

# Alarm volume by ICU unit
print(summary.groupby("icu_unit_type")["alarms_per_patient_day"].mean())

Hugging Face Datasets

from datasets import load_dataset

ds = load_dataset("xpertsystems/hlt009-sample", data_files={
    "vitals":      "vitals_timeseries.csv",
    "alarms":      "alarm_events.csv",
    "interventions": "interventions.csv",
    "summary":     "episode_summary.csv",
})
print(ds)

Vital sign trajectory plot

import pandas as pd
import matplotlib.pyplot as plt

vitals = pd.read_csv("vitals_timeseries.csv", parse_dates=["timestamp"])

# Plot HR + SpO2 trajectory for one episode
ep_id = vitals["episode_id"].iloc[0]
ep = vitals[vitals["episode_id"] == ep_id].sort_values("timestamp")

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6), sharex=True)
ax1.plot(ep["timestamp"], ep["hr_bpm"], color="#c44")
ax1.set_ylabel("HR (bpm)")
ax1.axhline(120, ls="--", color="grey", alpha=0.5)  # HIGH_HR threshold
ax2.plot(ep["timestamp"], ep["spo2_pct"], color="#4488ff")
ax2.set_ylabel("SpO2 (%)")
ax2.axhline(90, ls="--", color="grey", alpha=0.5)   # LOW_SPO2 threshold
ax2.set_xlabel("Time")
plt.suptitle(f"Vitals for episode {ep_id}")
plt.show()

Deterioration prediction baseline

import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split

vitals = pd.read_csv("vitals_timeseries.csv", parse_dates=["timestamp"])
summary = pd.read_csv("episode_summary.csv")

# Build a feature matrix at episode level from first-4h vitals
first_4h_features = []
for ep_id, ep in vitals.groupby("episode_id"):
    ep_sorted = ep.sort_values("timestamp")
    # Use first 48 timesteps = first 4 hours at 5-min resolution
    first_4h = ep_sorted.head(48)
    if len(first_4h) >= 12:
        first_4h_features.append({
            "episode_id": ep_id,
            "hr_mean": first_4h["hr_bpm"].mean(),
            "hr_std":  first_4h["hr_bpm"].std(),
            "spo2_min": first_4h["spo2_pct"].min(),
            "rr_max":   first_4h["rr_bpm"].max(),
            "news2_max_first4h": first_4h["news2_score"].max(),
            "news2_mean_first4h": first_4h["news2_score"].mean(),
        })

feats = pd.DataFrame(first_4h_features).merge(
    summary[["episode_id", "apache2_score", "sofa_score", "cci_score",
             "ventilation_status", "deterioration_6h_label"]],
    on="episode_id"
)

X = feats.drop(["episode_id", "deterioration_6h_label"], axis=1).fillna(0)
y = feats["deterioration_6h_label"]
if y.nunique() > 1:
    Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.3, random_state=42)
    m = GradientBoostingClassifier(random_state=42).fit(Xtr, ytr)
    print(f"6h deterioration AUC: {m.score(Xte, yte):.3f}")

Alarm fatigue analysis

import pandas as pd

summary = pd.read_csv("episode_summary.csv")
alarms = pd.read_csv("alarm_events.csv")

# Fatigue index by trajectory
print(summary.groupby("trajectory")[
    ["alarms_per_patient_day", "true_alarm_rate", "alarm_override_rate",
     "fatigue_index_score"]
].mean().round(3))

# False alarm subtypes
print(alarms[alarms["true_alarm_flag"] == 0]["false_alarm_subtype"]
      .value_counts())

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Suggested use cases

• 6-hour deterioration prediction — predict deterioration_6h_label from first-N-hour vitals + summary features
• Alarm fatigue research — analyze actionable vs nuisance alarm patterns, build false-alarm classifiers
• Sepsis prediction — train models on vital trajectories + qSOFA + NEWS2 trends
• ICU mortality risk — predict in_hospital_mortality from baseline severity + early vital features
• Mechanical ventilation prediction — predict ventilation onset from vital trajectories
• NEWS2 / qSOFA validation — test calibration of early warning scores in ML-augmented pipelines
• Signal quality / artifact classification — train artifact detectors using sqi_* and artifact_flag labels
• Time-series anomaly detection — vital sign outlier detection, change-point detection
• Multi-stream time-series modeling — joint LSTM/Transformer modeling on 12 simultaneous vital streams
• Alarm cascade analysis — alarm propagation and crash-cart event prediction
• Healthcare AI MLOps — pipeline testing for streaming ICU data, real-time inference rehearsal
• Educational use in critical care medicine and biomedical engineering

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Sample vs. full product

Aspect	This sample	Full HLT-009 product
Episodes	25	10,000+ (default) up to 1M
Settings	ICU only	mixed (ICU + RPM) configurable
Time resolution	5-min	1-min or 5-min
Schema	identical	identical
Calibration	identical	identical
License	CC-BY-NC-4.0	Commercial license

The full product unlocks:

• Up to 1M episodes for production-grade deterioration / sepsis / alarm fatigue model training
• RPM (Remote Patient Monitoring) episodes with multi-week outpatient monitoring (7-91 days)
• 1-min resolution for high-frequency analysis
• Mixed ICU+RPM for cross-care-setting model training
• Commercial use rights

Contact us for the full product.

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Limitations & honest disclosures

• Sample is preview-only. 25 episodes × ~27K timesteps is enough to demonstrate schema and calibration, but is not statistically sufficient for training deterioration prediction or sepsis classifiers. Use the full product (10K+ episodes) for serious ML work.
• ICU-only in this sample, not mixed setting. RPM episodes average 7-91 days × 288 timesteps/day = ~14K rows each, which would push the sample past 20 MB. The full product supports mixed ICU + RPM cohorts.
• Sample is on the larger side (5.3 MB) because continuous vital sign data has natural fan-out — each multi-day ICU episode produces ~1,000-3,000 timesteps at 5-min resolution. The full product scales linearly with episode count.
• Alarm priority diversity limited at this sample scale. The schema supports 4 priority levels (LOW/MEDIUM/HIGH/CRITICAL per IEC 60601-1-8), but at n=25 only MEDIUM+HIGH alarm types fire reliably. CRITICAL alarms (CRITICAL_LOW_HR, APNEA, CRITICAL_LOW_SPO2) require extreme physiology that's rare in stable cohorts. LOW priority alarms (HIGH_CVP, HIGH_CO) are also rare. The full product produces all 4 levels at scale.
• Vital signs are simulated, not real waveform data. Each timestep value is sampled from physiologically-realistic distributions calibrated to MIMIC-IV ranges. This is appropriate for ML algorithm development, but does NOT capture the full beat-to-beat waveform variability of real continuous monitoring. Real waveforms exhibit autocorrelation, R-R interval variability, and respiratory modulation that this synthetic data does not fully reproduce.
• 5-minute resolution, not beat-to-beat. The full product supports 1-min resolution; production ICU monitors sample at 125-500 Hz (waveform-level). For HRV / arrhythmia / respiratory waveform analysis, real waveform data is required.
• Mortality rate runs slightly low at this sample size (4-16% vs MIMIC-IV target 8-15%). At n=25 episodes this is 1-4 deaths total, so seed-to-seed variance is high. The full product hits 10-12% mortality reliably.
• Ventilation rate runs slightly high (~50% vs target 30-45%). This is a generator parameter (is_ventilated = rng.random() < 0.42) — the actual draw varies seed-to-seed.
• Synthetic, not derived from real ICU records. Vital sign distributions, alarm patterns, and severity scores follow published critical care references but do NOT reflect any specific real patient cohort.

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Ethical use guidance

This dataset is designed for:

• ICU deterioration prediction methodology development
• Alarm fatigue research and false-alarm classifier development
• Sepsis / NEWS2 / qSOFA validation methodology
• Continuous monitoring AI pipeline testing
• Educational use in critical care medicine and biomedical informatics
• Healthcare AI pretraining for time-series clinical prediction

This dataset is not appropriate for:

• Making clinical decisions about real patients
• FDA-regulated AI/SaMD device training (use real de-identified clinical data)
• Real-time alarm system tuning without separate validation
• Discriminatory analyses targeting protected demographic groups

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Companion datasets in the Healthcare vertical

• HLT-001 — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
• HLT-002 — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles)
• HLT-003 — Synthetic Clinical Trial Dataset (3 endpoint types + power sweep)
• HLT-004 — Synthetic Disease Progression Dataset (NSCLC + Heart Failure longitudinal)
• HLT-005 — Synthetic Hospital Admission Dataset (5K admissions + bed utilization)
• HLT-006 — Synthetic Medical Imaging Dataset (1K studies + COCO annotations + reports)
• HLT-007 — Synthetic Drug Response Dataset (3K patient-treatments × 25 drug classes + PGx + PK)
• HLT-008 — Synthetic Healthcare Claims Dataset (500 members + 30K claims + fraud labels)
• HLT-009 — Synthetic Continuous Vital Sign Monitoring Dataset (you are here)

Use HLT-001 through HLT-009 together for the full healthcare ML data stack: population → EHR → trials → progression → hospital ops → imaging → pharmacology → claims → continuous monitoring.

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Citation

If you use this dataset, please cite:

@dataset{xpertsystems_hlt009_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HLT-009 Synthetic Continuous Vital Sign Monitoring Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hlt009-sample}
}

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Contact

• Web: https://xpertsystems.ai
• Email: pradeep@xpertsystems.ai
• Full product catalog: Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more

Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.