Stanford Deidentifier Only Radiology Reports

🚀 斯坦福去標識符工具

斯坦福去標識符工具在多種放射學和生物醫學文檔上進行了訓練，旨在自動化去識別過程，同時達到足以用於生產環境的令人滿意的準確率。相關論文正在發表中。

🚀 快速開始

該工具主要用於放射學和生物醫學文檔的去識別處理，通過訓練模型自動檢測並替換受保護的健康信息（PHI）實體。

✨ 主要特性

• 多領域適用性：在多種放射學和生物醫學文檔上進行訓練，適用於不同類型的醫療報告。
• 高精度：在多個測試集上取得了較高的F1分數，能夠準確檢測和替換PHI實體。
• 自動化處理：實現了去識別過程的自動化，提高了處理效率。

📚 詳細文檔

相關數據集

使用了radreports數據集進行訓練。

技術標籤

該項目涉及以下技術標籤：

• token-classification
• sequence-tagger-model
• pytorch
• transformers
• pubmedbert
• uncased
• radiology
• biomedical

示例報告內容

以下是一些示例報告的內容：

• 檢查流程：胸部X光檢查。對比：上次檢查於2020年1月1日，還有2019年3月1日的記錄。檢查結果：片狀肺野模糊影。印象：2020年1月1日的胸部X光檢查結果最令人擔憂。患者被轉至UH醫療中心的另一個科室，由Perez醫生負責。我們於2020年2月1日使用MedClinical數據傳輸系統發送了數據，ID為5874233。我們收到了Perez醫生的確認信息。他的聯繫電話是567 - 493 - 1234。
• Curt Langlotz醫生選擇在6月23日安排一次會議。

關聯倉庫

關聯的GitHub倉庫：https://github.com/MIDRC/Stanford_Penn_Deidentifier

📄 許可證

本項目採用MIT許可證。

📚 引用

如果您使用了本項目，請引用以下論文：

@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},
}