license: cc-by-nc-4.0
language:
- pt
- en
tags:
- rare-disease
- patient-trajectory
- benchmark
- disease-progression
- brazilian-sus
- datasus
- world-model
- time-to-event
pretty_name: RareBench-BR Trajectory v2
task_categories:
- time-series-forecasting
- tabular-classification
size_categories:
- 100K<n<1M
RareBench-BR Trajectory v2 (RBT-v2)
The first rare-disease patient-trajectory benchmark designed to be autocorrelation-immune. Built from 44,051 real CNS-linked Brazilian SUS (DATASUS) rare-disease treatment trajectories. Five tasks, balanced/stratified splits, geographic-external test, strong count-based baselines, full datasheet.
Authors: Raras AI · License: CC-BY-NC 4.0 · Contact: dimas@raras.ai Companion architecture: Raras-AI/gemeo-arch
Why this benchmark exists
Patient-trajectory prediction on EHR data is dominated by event autocorrelation: in a rare-disease orphan-drug trajectory, ~82% of events are repeats of the same monthly dispensing code. A model that just copies the patient's last code scores near-perfectly on naive next-event tasks — the documented "repeated event tokens inflate metrics" pitfall (RAVEN, arXiv 2603.24562). There was no public rare-disease trajectory benchmark, and our own first attempt (an L6 next-event track) was 99.5% one class — trivially won by "always repeat".
RBT-v2 is built so that a repeat-last baseline cannot win: the core task scores only transition points (where the trajectory actually changes), binary tasks are balanced 50/50, and we ship the autocorrelation oracle as an explicit baseline so you can see it fail.
The five tasks
| Task | Definition | Metric | Why it's hard |
|---|---|---|---|
| T1 — Next-proc @ transition | Given a prefix, predict the next procedure code at a point where the trajectory changes (switch / gap-resumption) | Recall@1/5, MRR | Repeats excluded — the autocorrelation oracle scores only 12.4% |
| T2 — Will-change | Will the next event be a change vs a continuation? (balanced 50/50) | Balanced acc, AUROC | Majority baseline = 50.0% by construction |
| T3 — New-onset | Predict the first occurrence of a procedure the patient has never had | Recall@1/5 | First-occurrences only (RAVEN-style) |
| T4 — Discontinuation | Will the patient discontinue therapy (>6-mo gap) within follow-up? (balanced) | Balanced acc, AUROC | Treatment dropout — clinically critical; majority = 50.0% |
| T5 — Time-to-transition | Months until the next treatment change (right-censored) | C-index, Brier | Genuine time-to-event / world-model capability |
Baselines (the bar to beat)
Computed on train, evaluated on test (95% bootstrap CI). Count-based
baselines are strong (per arXiv 2511.00782) — beating the bigram is the real bar.
| Task | Baseline | test |
|---|---|---|
| T1 | frequency Top-1 | 26.9% [25.8, 28.0] |
| T1 | bigram Top-1 | 64.4% [63.2, 65.6] ← the bar |
| T1 | bigram Top-5 | 96.0% [95.5, 96.5] |
| T1 | repeat-last (autocorrelation oracle) | 12.4% [11.6, 13.2] ← fails by design |
| T2 | majority / always-continue | 50.0% |
| T3 | frequency Top-1 / Top-5 | 17.5% / 57.9% |
| T4 | majority | 50.0% |
The repeat-last oracle scoring 12.4% (not ~99%) is the proof of autocorrelation-immunity. A real model must beat the bigram, not just copy.
GEMEO leads every novelty & long-context task
The flagship gemeo-sus
world model (recurrence-aware) sets the current bar — see LEADERBOARD.md:
| Task | GEMEO | Strong baseline | Margin |
|---|---|---|---|
| New-onset prediction (Top-1) | 53.7% | 38.2% (frequency) | +15.5 pp |
| T2 — will-change (AUROC) | 0.906 | 0.889 (count-based) | +0.017 |
| T5 — transition-within-12mo (AUROC) | 0.827 | 0.790 (count-based) | +0.037 |
| T4 — treatment discontinuation (AUROC) | 0.838 | 0.696 (count-based) | +0.142 |
The world model's learned representation pulls clearly ahead on the context-rich tasks that matter most in rare disease. For single-step Markov transitions (T1), the count-based bigram remains near-optimal — the expected task-dependent split (arXiv 2511.00782).
Data provenance & ethics
- Source: DATASUS APAC-SIA (high-complexity outpatient, orphan-drug authorizations), CNS-hash linked. 7 Brazilian states, 2017–2021.
- Cohort: 44,051 patients with ≥5 treatment events; 11 rare diseases (Gaucher, MPS I/II, SMA, DMD, CF, Wilson, Friedreich, Marfan, NF1, Rett, …); 33 distinct SIGTAP procedure codes.
- De-identification: ages bucketed, UF only (no município), CNS hashed, k-anonymity ≥ 5.
- Ethics: Brazilian Res. CNS 466/2012 + 510/2016; LGPD-compliant.
- Splits: patient-level 70/15/15 (train/val/test) on the 5 largest UFs + a geographic-external test on the remaining states (external validity / equity probe; note ext_test is small, n≈22 patients — interpret as a probe, not a powered test).
Files
tasks/
├── T1_next_proc_transition.{train,val,test,ext_test}.jsonl
├── T2_will_change.{...}.jsonl (balanced 50/50)
├── T3_new_onset.{...}.jsonl
├── T4_discontinuation.{...}.jsonl (balanced 50/50)
└── T5_time_to_transition.{...}.jsonl
baselines.json # all baseline numbers + bootstrap CI
stats.json # cohort + vocab statistics
DATASHEET.md # full datasheet-for-datasets
Each case: {case_id, split, orpha, sex, uf, prefix_procs:[...], target:..., ...}.
How to evaluate your model
- Train on
*.train.jsonl(+*.val.jsonl). - Predict on
*.test.jsonl(and*.ext_test.jsonlfor external validity). - Report the metric in the table above with bootstrap 95% CI.
- You must report the repeat-last and bigram baselines alongside your model — a result that doesn't beat the bigram on T1 is not a positive result.
Citation
@misc{rarebench_br_trajectory_v2_2026,
title = {RareBench-BR Trajectory v2: An Autocorrelation-Immune
Rare-Disease Patient-Trajectory Benchmark from Brazilian SUS},
author = {Timmers, Dimas and the Raras AI team},
year = {2026},
url = {https://huggingface.co/datasets/Raras-AI/rarebench-br-trajectory},
note = {First public rare-disease trajectory benchmark. CC-BY-NC 4.0.}
}
⚠️ Research only. Not a medical device. Derived from de-identified aggregate SUS data.