| --- |
| 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](https://huggingface.co/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`](https://huggingface.co/Raras-AI/gemeo-sus) |
| world model (recurrence-aware) sets the current bar — see [`LEADERBOARD.md`](./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 |
| |
| 1. Train on `*.train.jsonl` (+ `*.val.jsonl`). |
| 2. Predict on `*.test.jsonl` (and `*.ext_test.jsonl` for external validity). |
| 3. Report the metric in the table above with bootstrap 95% CI. |
| 4. **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 |
|
|
| ```bibtex |
| @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. |
|
|
