ArticleNPJ digital medicine2020
Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
66 citing papers in PubMed, 101 citations in OpenAlex.
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- Divergence in Menopause Symptom Narratives Between Online and Clinical Settings.JAMA network open · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Benchmarking GPT-5, LLaMA, and Mistral for Clinical Named Entity Recognition in Ophthalmology Progress Notes.Translational vision science & technology · 2026Article
- Assessing genetic counseling efficiency with natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Development and Validation of a Large Language Model Case Identification Strategy for Eosinophilic Esophagitis.Gastro hep advances · 2026Article
- A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2026Article
- Clinically informed semi-supervised learning improves disease annotation and equity from electronic health records: a glaucoma case study.NPJ digital medicine · 2025Article
- Large language models accurately identify immunosuppression in intensive care unit patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Clinically Informed Semi-Supervised Learning Improves Disease Annotation and Equity from Electronic Health Records: A Glaucoma Case Study.Research square · 2025Article
- MedVidDeID: Protecting privacy in clinical encounter video recordings.Journal of biomedical informatics · 2025Article
- Clinically Informed Semi-Supervised Learning Improves Disease Annotation and Equity from Electronic Health Records: A Glaucoma Case Study.medRxiv : the preprint server for health sciences · 2025Article
- Small language models learn enhanced reasoning skills from medical textbooks.NPJ digital medicine · 2025Article
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- LEVERAGING UNSTRUCTURED DATA IN ELECTRONIC HEALTH RECORDS TO DETECT ADVERSE EVENTS FROM PEDIATRIC DRUG USE - A SCOPING REVIEW.medRxiv : the preprint server for health sciences · 2025Article
- De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.BMC medical informatics and decision making · 2025Article
- pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.JAMIA open · 2025Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.