Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 364, in tsfile.tsfile_reader.TsFileReaderPy.__init__
                File "tsfile/tsfile_reader.pyx", line 367, in tsfile.tsfile_reader.TsFileReaderPy.init_reader
                File "tsfile/tsfile_py_cpp.pyx", line 789, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
                File "tsfile/tsfile_py_cpp.pyx", line 55, in tsfile.tsfile_py_cpp.check_error
              tsfile.exceptions.FileOpenError: 28: Failed to open file
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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

Apache TsFile version of xpertsystems/hlt009-sample.

Overview

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.

  • Tables: 4 TsFile table(s), 26,888 rows in total.
    • alarm_events: 166 rows, 2022-03-19 15:20:00 → 2023-09-19 22:15:00 (UTC)
    • episode_summary: 25 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.024000 (UTC)
    • interventions: 20 rows, 1970-01-01 00:00:00 → 2023-09-19 14:23:03.165000 (UTC)
    • vitals_timeseries: 26,677 rows, 2022-03-15 00:00:00 → 2023-12-07 06:30:00 (UTC)

Schema (TsFile structure)

alarm_events.tsfile

  • Time (INT64, milliseconds) — the source alarm_onset_ts column.
  • TAG: none — the table is stored as a single device.
  • FIELD (14): alarm_id (string), episode_id (string), alarm_type (string), alarm_priority (string), alarm_duration_sec (int64), true_alarm_flag (int64), false_alarm_subtype (string), response_time_min (double), intervention_triggered (int64), override_flag (int64), limit_at_alarm_low (double), limit_at_alarm_high (double), alarm_cascade_id (int64), shift (string)

episode_summary.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (54): episode_id (string), monitoring_setting (string), icu_unit_type (string), admit_dt (string), discharge_dt (string), episode_duration_days (double), bed_id (string), age (int64), sex (string), apache2_score (int64), sofa_score (int64), sofa_at_discharge (int64), primary_dx_group (string), cci_score (int64), ventilation_status (int64), vasopressor_flag (int64), rrt_flag (int64), has_arterial_line (int64), has_central_line (int64), has_pa_catheter (int64), trajectory (string), monitor_manufacturer (string), rpm_device_type (double), lead_configuration (string), device_uptime_pct (double), connectivity_drops (int64), true_alarm_rate (double), total_alarms (int64), alarms_per_patient_day (double), actionable_alarm_rate (double), alarm_override_rate (double), median_response_time_min (double), alarm_limit_modification_count (int64), alarm_cascade_count (int64), fatigue_index_score (double), news2_max (double), news2_mean (double), qsofa_max (int64), deterioration_6h_label (int64), in_hospital_mortality (int64), readmission_30d (int64), rapid_response_event (int64), sqi_hr_bpm (double), sqi_spo2_pct (double), sqi_rr_bpm (double), sqi_nbp_sys_mmhg (double), sqi_nbp_dia_mmhg (double), sqi_nbp_map_mmhg (double), sqi_ibp_sys_mmhg (double), sqi_ibp_dia_mmhg (double), sqi_temp_c (double), sqi_etco2_mmhg (double), sqi_cvp_mmhg (double), sqi_cardiac_output_lpm (double)

interventions.tsfile

  • Time (INT64, milliseconds) — the source intervention_ts column.
  • TAG: none — the table is stored as a single device.
  • FIELD (10): intervention_id (string), episode_id (string), intervention_type (string), alarm_trigger_id (string), latency_min (double), drug_class (string), dose_delta (double), vent_param_changed (string), rosc_flag (double), rapid_response_team_id (double)

vitals_timeseries.tsfile

  • Time (INT64, milliseconds) — the source timestamp column.
  • TAG: none — the table is stored as a single device.
  • FIELD (18): episode_id (string), hr_bpm (double), spo2_pct (double), rr_bpm (double), nbp_sys_mmhg (double), nbp_dia_mmhg (double), nbp_map_mmhg (double), ibp_sys_mmhg (double), ibp_dia_mmhg (double), temp_c (double), etco2_mmhg (double), cvp_mmhg (double), cardiac_output_lpm (double), artifact_flag (int64), news2_score (double), qsofa_score (double), news2_roll_max_4h (double), news2_rate_of_rise (double)

Conversion notes

  • One TsFile per source CSV (4 tables). Every table is stored as a single device, so all source columns (episode/acility ids, alarm ids, device vendors) are FIELDs.
  • Time = the source timestamp column parsed as INT64 milliseconds: timestamp (5-minute vitals grid), alarm_onset_ts, intervention_ts. episode_summary is an episode-level table with no per-row clock, so its Time is a synthetic row index (0..N-1).
  • No column is dropped.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("alarm_events.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

  • Original dataset: xpertsystems/hlt009-sample
  • Source card title / publisher: HLT-009 Synthetic Continuous Vital Sign Monitoring Dataset (Sample Preview)
  • License: cc-by-nc-4.0 (as declared by the original dataset).
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