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README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+task_categories:
+  - text2text-generation
+  - table-question-answering
+tags:
+  - text-to-sql
+  - clinical
+  - fhir
+  - healthcare
+  - synthetic
+  - duckdb
+  - synthea
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train.jsonl
+      - split: heldout_familiar
+        path: heldout_familiar.jsonl
+      - split: heldout_unseen
+        path: heldout_unseen.jsonl
+---
+
+# FHIR-to-SQL with Database-Resolved Clinical Terminology
+
+Natural-language hospital questions paired with a **structured JSON query plan and compiled DuckDB
+SQL**, over a FHIR-derived clinical schema. Built entirely from **synthetic** Synthea patients — no
+real patient data.
+
+Companion to the paper *Plan-Then-Compile: Turning a General-Purpose Coder Model into a FHIR Data
+Analyst*.
+
+- **Code & paper:** https://github.com/adelelsayed/fhirsql-reasoning-sql
+- **Adapters:** https://huggingface.co/adelelsayed1991/fhirsql-reasoning-sql-adapters
+
+## What makes this dataset different
+
+Gold SQL **never embeds a literal clinical code**. Every query resolves its concept at runtime
+through a lookup against a `valuesets` terminology table:
+
+```sql
+WITH resolved AS (
+    SELECT code, code_system FROM valuesets
+    WHERE table_name = 'procedure' AND display ILIKE '%Depression screening (procedure)%'
+)
+SELECT DISTINCT patient_id
+FROM procedure, resolved
+WHERE procedure.code = resolved.code AND procedure.system = resolved.code_system
+  AND status = 'completed'
+```
+
+This turns terminology resolution from a memorization problem into a queryable one, and makes
+generalization to clinical concepts absent from training measurable.
+
+## Splits
+
+| Split | Rows | Answerable | Abstention |
+|---|---:|---:|---:|
+| `train` | 10,696 | 9,680 | 1,016 |
+| `heldout_familiar` | 9,300 | 8,284 | 1,016 |
+| `heldout_unseen` | 2,156 | 1,140 | 1,016 |
+
+Held-out splits come from a **disjoint 6,383-patient population** (training: 18,999 patients; zero
+`patient_id` overlap).
+
+- **`heldout_familiar`** — clinical concepts also used in training, different patients. Isolates
+  population generalization. **96.5% of its answerable rows are byte-identical `(question, gold_sql)`
+  pairs from `train`**, so it measures whether learned queries stay correct on new data; it cannot
+  distinguish memorization from generalization.
+- **`heldout_unseen`** — 84 clinical concepts appearing **nowhere** in training (verified by set
+  intersection; zero shared question/query pairs). This is the split for measuring transfer.
+
+⚠️ **The 1,016 abstention rows are the same rows in all three splits.** Unanswerable questions
+reference no patient data, so an identical set was reused. Abstention metrics computed on the
+held-out splits therefore measure *retention* of trained refusal behavior, not held-out refusal
+generalization.
+
+⚠️ **Execution match is weakly discriminative on `heldout_unseen`.** Its concepts are rare (1–3
+patients each) and gold answers are small integers: 53.9% of within-archetype concept pairs return
+identical results, so a wrong concept can go undetected. Text-match metrics are not subject to this.
+
+## Fields
+
+| Field | Description |
+|---|---|
+| `question` | Natural-language question, phrased for one of 11 hospital roles |
+| `target` | Training target: `json.dumps(plan, indent=2)` + fenced ` ```sql ` block |
+| `gold_plan` | Structured query plan (entities, joins, constraints, aggregation, abstain) |
+| `gold_sql` | Compiled DuckDB SQL, or the literal `UNANSWERABLE` token |
+| `archetype_id` | Query archetype (82 total: 64 answerable + 18 unanswerable) |
+| `tier` | Difficulty tier 1–4, or 5 for unanswerable |
+| `persona` | Hospital role the phrasing targets |
+| `concept_display` | The clinical concept instantiated (empty for structural/unanswerable) |
+| `schema_ref` | Schema the query is written against |
+
+## Provenance
+
+Every answerable row's gold SQL was **executed against the real database** and kept only if it ran
+without error and returned at least one row, then checked for repeated-execution stability.
+Questions come from 160 role-appropriate phrasings authored with LLM assistance under the author's
+direction, then parameterized by concept.
+
+`schema.sql` is the exact schema injected into every prompt. `valuesets_ddl.sql` is an ablation
+fragment that was **not** part of the prompt during the published runs — see the paper, §5.6.
+
+## Limitations
+
+Synthetic data only; one question-generation distribution (templated phrasing, not free-form
+clinician language); terminology resolution is substring matching against a corpus-derived
+dictionary with one display string per code, so synonymy, hierarchy, and ambiguity are out of scope
+(ambiguous concepts are excluded from gold). Some regulatory questions say "dispensed" but are
+answered from `MedicationRequest` (prescription orders). Full discussion in the paper's §7.
+
+## Citation
+
+See `CITATION.cff` in the GitHub repository.