---
pretty_name: RealICU
license: other
license_name: physionet-credentialed-health-data-license-1.5.0
license_link: https://physionet.org/about/licenses/physionet-credentialed-health-data-license-150/
language:
- en
tags:
- medical
- clinical
- intensive-care
- electronic-health-records
- benchmark
- llm-agents
size_categories:
- n<1K
configs:
- config_name: icu_stays
  data_files: trajectories/icu_stay.parquet
- config_name: events
  data_files: trajectories/events.parquet
---

# RealICU

[Paper](https://arxiv.org/abs/2605.13542) · [Code](https://github.com/chengzhi-leo/RealICU-Bench) · [Project page](https://chengzhi-leo.github.io/RealICU-Bench/)

> [!IMPORTANT]
> **This repository contains a synthetic sample, not the benchmark data.** The files here describe 5
> synthetic patients in exactly the same file layout, Parquet schemas, and label JSON format as the full
> dataset. They contain no patient data: every identifier, date, measurement, note, and label was generated
> from hand-written scenarios, not from MIMIC records. Use the sample to inspect the format and to test data
> pipelines; do not use it to evaluate models.
>
> **The full dataset is distributed through PhysioNet.** RealICU is derived from MIMIC-IV, so it is released
> under PhysioNet credentialed access: a credentialed PhysioNet account, completed human-subjects research
> training (CITI), and a signed data use agreement. It has been submitted to PhysioNet as
> *MIMIC-IV-Ext-RealICU* and is under review. The PhysioNet link will be added here once it is published.

RealICU is a benchmark for evaluating language models and agents on ICU decision support. For each
evaluation window, it provides clinical events for constructing model inputs and aligned reference labels
for four tasks: patient status, acute problems, recommended actions, and red flag actions.

The full dataset (version 1.0.0) contains 94 MIMIC-derived patients, one target ICU stay per patient, and
2,989,158 clinical events. The trajectories include available records before, during, and after the target
ICU stay.

| Annotation set | Reference source | Windows | Action ratings |
| --- | --- | ---: | ---: |
| RealICU-Gold | Paired physician consensus on Oracle candidates | 930 | 2,189 |
| RealICU-Scale | Oracle, an LLM hindsight annotator | 11,862 | 25,387 |

Gold and Scale share the same patients and have partially overlapping windows; Gold is not a window subset
of Scale. Both are evaluation sets and should not be used as separate training and test cohorts. Windows
generally span 30 minutes, with shorter intervals at the end of a stay when needed.

## Synthetic sample in this repository

| Content | Sample | Full dataset |
| --- | ---: | ---: |
| Patients (one target ICU stay each) | 5 | 94 |
| Clinical events | 1,743 | 2,989,158 |
| Gold windows / action ratings | 35 / 46 | 930 / 2,189 |
| Scale windows / action ratings | 198 / 219 | 11,862 / 25,387 |

The five synthetic stays cover septic shock, hypoxemic respiratory failure, diabetic ketoacidosis, upper
gastrointestinal bleeding, and decompensated heart failure. They also reproduce the format's edge cases: a
truncated final window, a Gold window with an unavailable patient-status label, a Scale window without
acute-problem references, an `insufficient_data` window, an ICU readmission, and in-hospital deaths after
ICU discharge. Synthetic identifiers start with 9 (for example `90000001_99000001`) and do not overlap
MIMIC-IV identifier ranges.

## Quick start

The standalone loader requires Python 3.10 or later, `pandas`, and `pyarrow`:

```bash
python -m pip install pandas pyarrow huggingface_hub
hf download leoshen/RealICU --repo-type dataset --local-dir realicu
cd realicu
```

Load one Gold window:

```python
from loader import RealICULoader

loader = RealICULoader(".", "gold", "90000001_99000001")
sample = loader.load_window(
    loader.window_indices[0],
    icu_history_hours=None,
    pre_icu_history_hours=48,
    excluded_event_codes=("NOTE_DISCHARGESUMMARY", "META_DEATH"),
)

model_input = sample["input"]
labels = sample["labels"]
current_events = model_input["current_events"]
references = labels["reference_labels"]
availability = labels["label_availability"]

print(current_events.head())
if availability["patient_status"]:
    print(references["patient_assessment"]["overall"]["label"])
```

Use `"scale"` instead of `"gold"` to read Oracle annotations. The loader reads a stay once; call
`load_window` for any ID in `loader.window_indices`. These IDs may be nonconsecutive. List available stays
with `pandas.read_parquet("trajectories/icu_stay.parquet")["stay_id"]`.

Each sample has two parts:

- `input`: sample and stay identifiers, actual window timestamps, and the three event tables below.
- `labels`: the four tasks under `reference_labels`, plus `label_availability` indicating which references
  can be used for evaluation.

| Input event table | Included observations |
| --- | --- |
| `pre_icu_events` | Before target ICU admission, within the chosen pre-ICU lookback |
| `history_events` | During the target ICU stay, before the window, within the chosen ICU lookback |
| `current_events` | Within the actual half-open interval `[window_start_time, window_end_time)` |

Both lookbacks and `excluded_event_codes` are required choices. Use a number of hours, `None` for all
available history, or `0` for no history. The pre-ICU lookback is measured from ICU admission; the ICU
lookback is measured from the window start. An empty tuple retains all event categories. The example's
48-hour lookback and exclusions are illustrative, not benchmark defaults.

The same example is available from the command line:

```bash
python loader.py --root . --label-set gold --stay-id 90000001_99000001 \
  --window-index 0 --icu-history-hours all --pre-icu-history-hours 48 \
  --excluded-event-codes NOTE_DISCHARGESUMMARY META_DEATH
```

The prediction and evaluation code in [RealICU-Bench](https://github.com/chengzhi-leo/RealICU-Bench) reads
this layout directly; set `data.root` to the downloaded directory.

## Tasks and reference labels

The paths below are relative to `reference_labels`:

| Task | JSON path | Reference content |
| --- | --- | --- |
| Patient Status | `patient_assessment.overall` | `stable`, `improving`, `deteriorating`, or `insufficient_data`, with a rationale |
| Acute Problems | `patient_assessment.active_risks` | Active clinical problems or emerging risks, with supporting evidence |
| Recommended Actions | `action_review.evaluations` | Recorded actions rated `best_practice`, `acceptable`, `potentially_harmful`, or `insufficient_data` |
| Red Flag Actions | `action_review.red_flags` | Actions to avoid, with a rationale and supporting evidence |

Actions rated `best_practice` or `acceptable` form the positive reference set for recommendation
evaluation. Every retained action refers to an event within its assigned window. Numeric action and
evidence IDs refer to `icu_event_index` within the same target stay.

Check `label_availability` for each task before scoring. Its keys are `patient_status`, `active_risks`,
`recommended_action`, and `red_flags`. A false value means the reference is unavailable. An unavailable
patient-status label is stored as `unavailable`; this is not a clinical class. An available
`insufficient_data` label remains a valid reference. An available empty list means no retained reference
items, not a comprehensive finding that no clinical problems or contraindications exist.

In the full dataset, Gold contains physician-consensus labels and selected items from Oracle candidates;
its `rationale` and `key_evidence` were retained from Oracle. Scale labels and explanations are Oracle
outputs. Annotations use retrospective context, so evidence may refer to events before or after the
observation window, or to pre-ICU history and discharge summaries.

## Files and alignment

```text
README.md
loader.py
croissant.json
trajectories/
    events.parquet
    icu_stay.parquet
gold/labels/<stay_id>.json
scale/labels/<stay_id>.json
```

`events.parquet` contains one clinical event per row. `icu_stay.parquet` contains one target ICU stay per
row, including admission/discharge times, trajectory bounds, event counts, and outcomes. Join trajectories
and annotations using `stay_id`, formatted as `<subject_id>_<icu_stay_id>`. An event's own `icu_stay_id`
may refer to a different encounter; use the added `stay_id` for joins.

Each annotation set has one JSON file per target stay. The file contains stay identifiers,
`trajectory_metadata`, and a `window_outputs` list. Each window has `sample_id`, `window_index`,
`window_key`, `window_metadata`, `reference_labels`, and `label_availability`. Use the actual timestamps in
`window_metadata` to align events; `window_key` gives nominal hours relative to ICU admission.

| Event identifier | Meaning |
| --- | --- |
| `event_idx`, `source_event_index` | Original source event identifier |
| `event_index` | Zero-based position in the published trajectory |
| `icu_event_index` | Zero-based chronological event ID within the target ICU stay; null outside it |

`croissant.json` provides Croissant 1.1 metadata with field descriptions and Responsible AI documentation.
Its record sets expose ICU stays, events, windows, action evaluations, active risks, and red flags; each
label record set covers Gold and Scale, distinguished by `label_set`. Its file URLs point to this
repository, so it can be loaded directly with `mlcroissant`.

## Evaluation input construction

Keep reference answers and retrospective outcomes out of model inputs. For Action Recommendation, the paper
removes recorded actions from the current window before prediction to prevent answer leakage (Appendix
A.1); the loader does not perform this masking automatically.

## Citation

```bibtex
@article{shen2026realicu,
  title={RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation},
  author={Shen, Chengzhi and Shen, Weixiang and Susetzky, Tobias and Chen, Chen (Cherise) and Li, Jun and Liu, Yuyuan and Zhang, Xuepeng and Gong, Zhenyu and Rueckert, Daniel and Pan, Jiazhen},
  journal={arXiv preprint arXiv:2605.13542},
  year={2026}
}
```