This talk examines the crucial need for de-identifying protected health information (PHI) in unstructured patient-level data to harness its potential while ensuring compliance with legal and privacy requirements. With an...
De-identification is detecting privacy-related entities in text, such as person, organization names, emails, and other contact data, and masking them with different techniques. This task, also called anonymization or redaction, can help you:...
The process of de-identifying protected health information (PHI) from unstructured medical notes is often essential when working with patient-level documents, such as physician notes. Using current state-of-the-art techniques, automated de-identification...
Intro Clinical documents and doctor’s notes are significant resources for clinical and pharma research. There are many publications and examples about clinical notes de-identification using rule-based and machine learning /...
Healthcare providers and well-established players in the healthcare space possess vast amounts of unstructured patient-level data. This data has tremendous value, yet it stays primarily untapped due to the sensitivity...
Demos
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