[PDF] HOW TO DE-IDENTIFY A LARGE CLINICAL CORPUS IN 10 DAYS
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DAY 5. ‣ Create a custom CRF model on validated corpus. ‣ Pre-annotate a set of 20 documents with CRF model. ‣ 2 annotators revise the automatic de- ...
Jul 4, 2023 · Objectives. Clinical notes are a veritable treasure trove of information on a patient's disease progression, medical history, and treatment ...
Dec 5, 2019 · We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based ...
Jan 30, 2020 · Our first scenario is a fully customized system: a healthcare organization employs human annotators to label a sufficiently large number of PHI ...
This paper summarizes lessons learned from building a system used to de-identify over one billion real clinical notes, in a fully automated way, that was ...
Oct 7, 2021 · It is practically not possible to de-identify large numbers of EHRs manually. Identifying PHI in unstructured EHRs is also critical for ...
Why get de-identified data? Examples: Develop a predictive model to identify patients at high risk for hypoglycemia, with covariates and outcome data.
Missing: corpus 10 days.
Automated de-identification of free-text medical records
bmcmedinformdecismak.biomedcentral.com › ...
Jul 24, 2008 · The process of de-identification generally involves scanning the corpus of medical records line by line to identify all occurrences of PHI. This ...
Objective (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical ...
May 16, 2020 · PHI Corpus is contained, encompasses also a considerably larger corpus with records from 512 clinical units from. Karolinska University ...