Stanford Deidentifier Only Radiology Reports

🚀 スタンフォード脱識別ツール

スタンフォード脱識別ツールは、放射線学や生物医学の様々な文書を対象にトレーニングされており、脱識別プロセスの自動化を目的とし、実運用に耐えうる十分な精度を達成します。

🚀 クイックスタート

Stanford de-identifierは、放射線学および生物医学文書の脱識別プロセスを自動化するために開発されました。以下は、文書の例です。

widget:
- text: "PROCEDURE: Chest xray. COMPARISON: last seen on 1/1/2020 and also record dated of March 1st, 2019. FINDINGS: patchy airspace opacities. IMPRESSION: The results of the chest xray of January 1 2020 are the most concerning ones. The patient was transmitted to another service of UH Medical Center under the responsability of Dr. Perez. We used the system MedClinical data transmitter and sent the data on 2/1/2020, under the ID 5874233. We received the confirmation of Dr Perez. He is reachable at 567-493-1234."
- text: "Dr. Curt Langlotz chose to schedule a meeting on 06/23."
tags:
- token-classification
- sequence-tagger-model
- pytorch
- transformers
- pubmedbert
- uncased
- radiology
- biomedical
datasets:
- radreports
language:
  - en
license: mit

✨ 主な機能

• 放射線学および生物医学文書の自動脱識別
• 保護された健康情報（PHI）の検出と代替
• 高い精度での脱識別

📚 ドキュメント

Stanford de-identifierは、様々な放射線学および生物医学文書を対象にトレーニングされ、脱識別プロセスを自動化することを目的としています。関連するGitHubリポジトリはこちらです。

📄 ライセンス

このプロジェクトはMITライセンスの下で公開されています。

📜 引用

@article{10.1093/jamia/ocac219,
    author = {Chambon, Pierre J and Wu, Christopher and Steinkamp, Jackson M and Adleberg, Jason and Cook, Tessa S and Langlotz, Curtis P},
    title = "{Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods}",
    journal = {Journal of the American Medical Informatics Association},
    year = {2022},
    month = {11},
    abstract = "{To develop an automated deidentification pipeline for radiology reports that detect protected health information (PHI) entities and replaces them with realistic surrogates “hiding in plain sight.”In this retrospective study, 999 chest X-ray and CT reports collected between November 2019 and November 2020 were annotated for PHI at the token level and combined with 3001 X-rays and 2193 medical notes previously labeled, forming a large multi-institutional and cross-domain dataset of 6193 documents. Two radiology test sets, from a known and a new institution, as well as i2b2 2006 and 2014 test sets, served as an evaluation set to estimate model performance and to compare it with previously released deidentification tools. Several PHI detection models were developed based on different training datasets, fine-tuning approaches and data augmentation techniques, and a synthetic PHI generation algorithm. These models were compared using metrics such as precision, recall and F1 score, as well as paired samples Wilcoxon tests.Our best PHI detection model achieves 97.9 F1 score on radiology reports from a known institution, 99.6 from a new institution, 99.5 on i2b2 2006, and 98.9 on i2b2 2014. On reports from a known institution, it achieves 99.1 recall of detecting the core of each PHI span.Our model outperforms all deidentifiers it was compared to on all test sets as well as human labelers on i2b2 2014 data. It enables accurate and automatic deidentification of radiology reports.A transformer-based deidentification pipeline can achieve state-of-the-art performance for deidentifying radiology reports and other medical documents.}",
    issn = {1527-974X},
    doi = {10.1093/jamia/ocac219},
    url = {https://doi.org/10.1093/jamia/ocac219},
    note = {ocac219},
    eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocac219/47220191/ocac219.pdf},
}