| --- |
| 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} |
| } |
| ``` |
|
|
