Recognizing drug mentions in Social Media. This blog post includes a description of the participation in BioCreative tasks for the joint research team by Konplik Health and HULAT research group from Universidad Carlos III de Madrid.
Electronic Health Records de-identification explained in 2 minutes. Watch our video and visit our new section for more contents.
Try out the de-identification demo at Konplik. Remove Personal Health Information from EHR in a precise and efficient manner. Natural Language process applied to the anonymization of medical records according to HIPAA
This post includes a detailed overview of the implementation of an anonymization system at Konplik Health.
Photo by Daria Nepriakhina on Unsplash Agile Anonymization methodology at Konplik Health through Artificial Intelligence (AI) In a previous post, we talked about the HIPAA Privacy Rule and how to remove Personal Health Information from Electronic Health Records...
Photo by National Cancer Institute on Unsplash Anonymization for non-structured content in EHR using Artificial Intelligence Health data privacy is the main goal of information systems in the healthcare domain. This has been the case since the very first stages in the...
NLP projects in healthcare are often dependent on Internet-based public sources such as the world wide web. These projects usually begin by extracting data from a variety of websites. We call this process “web scraping” (or “web harvesting”). While users can handle...
Health and pharma companies have a good opportunity to leverage unstructured information. Text-based sources can be used to identify the costs of medical treatments, their efficiency (cost, benefits, and risks), references to drugs, side effects, patient experience,...
The modern definition of "patient-centered health care" was stated in the National Library of Medicine’s MED-LINE subject heading (MeSH), introduced in 1995, which reads, “Design of patient care wherein institutional resources and personnel are organized around...