Natural Language Processing (NLP) methods are increasingly used to automate various tasks in the processing of medical and biological texts, and manually annotated text corpora are required for the development and evaluation of these methods. To assist in the creation of richly annotated biomedical corpora, text annotation tools need to integrate existing NLP methods, ontologies and database resources and provide users with an accessible interface for visualising and editing complex structured annotations.
Substantial amounts of clinically significant information such as symptoms and other personal details expressed by the patient to the provider are contained only within clinical electronic medical record notes. The field of clinical informatics is devoted to developing methods to accurately retrieve salient information from text for quality improvement, research, and population health. There are limited open source common platforms that perform these tasks. We present V3NLP Framework and LEO as an open source suite of functionalities to build such applications.
Named-Entity Recognition (NER) is an important and difficult problem that is likely to remain an ongoing research area in biomedical informatics for the foreseeable future. The MetaMap system, developed by Dr. Alan (Lan) R. Aronson at the Lister Hill National Center for Biomedical Communications in the National Library of Medicine (NLM), is a well-known and often-cited NER application used the world over in academia, industry, government, and hospitals to analyze text drawn from both the biomedical literature (e.g., MEDLINE articles) and clinical reports.
AMIA People and Organizational Issues Working Group, AMIA Evaluation Working Group (WG), with participation from the IMIA Technology Assessment & Quality Development in Health Informatics WG and the EFMI Evaluation WG present this webinar.
Natural language processing (NLP) is increasingly used to tackle the huge volume of information available in both the free text of medical records, and in the life science literature.
To create interoperability across regions in LMICs a practical architecture solution for all partners to follow must exist. OpenHIE empowers LMICs to pragmatically implement sustainable health information sharing architectures that measurably improve health outcomes. In this webinar, participants will be introduced to OpenHIE and encouraged to discuss its use.
After participating in this activity, the learner should be better able to:
The complexity of patient care data magnifies the importance of implementing consistent, critical, structured data elements within electronic patient records. Interoperability and data sharing are reliant on data standardization; however, health care data management presents unique challenges in an environment of continual change.
The Informatics Paper Club of the Air presented by the AMIA CIS-WG is a regularly scheduled Paper Club series that will address the gap in knowledge and performance by an ongoing review of literature and by exposing our clinical informaticists to evidence-based approaches and strategies with discussions centered on incorporating those strategies into their practices.
The theme for the next few episodes will be open notes. The following two papers will be discussed during this webinar:
The reuse of EHR data is a promising complement to traditional, prospective approaches to various activities involved in healthcare improvement, including, but not limited to, clinical research and the evaluation and improvement of care. Existing clinical data sources provide opportunities for efficient analyses and can be expected to be representative of the populations of interest.
Rapid growth in the clinical implementation of large electronic medical records (EMRs) has led to an unprecedented expansion in the availability of dense longitudinal datasets for clinical and translational research. Secondary use of EMR data for clinical and translational research is hampered by the fact that much of detailed patient information is embedded in narrative text. Natural Language Processing (NLP) technologies, which are able to convert unstructured clinical text into coded data, have been introduced into the biomedical domain and have demonstrated promising results.