ehrextract

Structured feature extraction from clinical notes. Three steps:

1. Bring your notes — CSV, JSONL, JSON, XLSX, plain text, or a pandas DataFrame.
2. Pick a task — a built-in task (comorbidity, clinical_vars, full) or your own YAML file with your own fields and prompt.
3. Pick a model — a fine-tuned LoRA adapter on a local base model, your own local HuggingFace weights, or an API model (OpenAI-compatible or Anthropic).

One command (or one function call) later you have a results table — CSV, JSONL, JSON, XLSX, or Parquet — with one column per extracted field.

Important — read before use. ehrextract is research-grade software. It is NOT a medical device, is NOT FDA-cleared / Health Canada-approved, and MUST NOT be used for clinical decision-making, patient triage, eligibility determination, re-identification, surveillance, or any setting where its outputs affect a person's access to care, insurance, employment, or legal status. Outputs may hallucinate; any research use requires per-row human review. The egress-warning system is informational, not a privacy compliance control. Users are solely responsible for HIPAA / PHIPA / PIPEDA / GDPR / REB compliance. See NOTICE for the full acceptable-use scope.

Install

pip install ehrextract                  # core (~50 MB)
pip install 'ehrextract[hf]'            # + torch + transformers + peft (~3 GB)
pip install 'ehrextract[openai]'        # + openai SDK
pip install 'ehrextract[anthropic]'     # + anthropic SDK

Python ≥ 3.11. For a development install from a clone, see CONTRIBUTING.md.

30-second example

ehrextract \
  --task comorbidity \
  --model Qwen/Qwen3.5-27B --adapter /path/to/adapter \
  --input notes.csv --output results.csv

or, as a library:

from pathlib import Path
from ehrextract import extract

df = extract(
    Path("notes.csv"),
    "comorbidity",
    model="Qwen/Qwen3.5-27B",
    adapter="/path/to/adapter",
    output="results.csv",
)

The input needs a note_text column (configurable via --text-column); a note_id column is added automatically when absent. The output has one column per task field plus parse_success, validation_errors, raw_response, finish_reason, repair_attempts, and token counts.

On API providers, --batch submits the whole run as one provider-side batch at 50% API cost, and --max-repairs N re-prompts the model with the exact field errors when a response fails to parse or validate. See quickstart.md.

Built-in tasks

Task	Fields	What it extracts
comorbidity	17	Free-text diagnosis list + 16 Y/N comorbidity categories
clinical_vars	4	Feeding and neurologic variables (tube/oral feeding, aspiration risk, NI trajectory)
full	20	Joint task: the 16 comorbidity categories + the 4 clinical variables

Built-in tasks ship inside the package; --task <name> works without any extra files. To extract your own variables, define a task in YAML — start with the step-by-step authoring-tasks.md guide (schema-reference.md is the full grammar).

Note on the full task. The full task enables constrained JSON decoding by default on the local HuggingFace provider — the same mechanism the research pipeline used for the published joint-task numbers, forcing structurally valid, schema-conformant output at the token level. It requires the [hf] extra (which includes lm-format-enforcer); disable it with --no-constrained. API providers ignore the setting (Anthropic already forces the schema via tool-use).

Data handling

If your input may contain PHI, read data-handling.md BEFORE running with any API provider. The package writes a data-egress notice to stderr (once per process per destination) on API use; it never blocks, and it does not (and cannot) guarantee compliance for you. The local HuggingFace provider keeps all data on your machine.

Documentation

• quickstart.md — fine-tuned adapters, custom tasks, API providers
• authoring-tasks.md — define your own variables: a step-by-step custom-task guide
• schema-reference.md — the task-file YAML reference
• data-handling.md — PHI, egress notice, BAA-eligible providers
• extending-providers.md — plug in a custom provider

Authors and institutions

ehrextract was developed by:

• Chen Zhang (lead author)
• Yibing Xia (co-author)
• Sanjay Mahant, MD -- supervisor, The Hospital for Sick Children (SickKids)
• Nathan Taback, PhD -- supervisor, University of Toronto

at The Hospital for Sick Children (Toronto, Canada) and the University of Toronto (Toronto, Canada). Please cite the project if you use it in published work.

License

Licensed under the Apache License, Version 2.0. See LICENSE for the full license text and NOTICE for attribution, the no-endorsement clause, the clinical-use disclaimer, and the acceptable-use restrictions that supplement (but do not override) the License.