The AMIA Evaluation Working Group and People and Organizational Issues Working Group are proud to present this webinar.
Natural language processing (NLP) techniques have been applied to investigate drug interactions and adverse drug events, but have limited applications to support dietary supplements research. The use of dietary supplements in the U.S. has dramatically increased in recent years, but our ability is currently limited to identify the potential interactions between dietary supplements and medications. Much related information is embedded in the unstructured data, such as biomedical literature and clinical notes.
Informatics research using social media, such as Facebook and Twitter, is a new frontier. Issues of privacy, IRB oversight and regulation and consent are still very much being debated. Some of this work is qualitative and some is quantitative and many studies are both. The purpose of this panel and webinar sponsored by the Evaluation and People and Organizational working groups is to explore the topic of Human Subjects using this kind of data with a panel of presenters that include: 2 researchers in the field, 1 IRB representative, and an editor of a qualitative journal.
The Critical Assessment of Genome Interpretation (CAGI, \'kā-jē\) is a community experiment to objectively assess computational methods for determining the phenotypic impacts of genomic variation. The primary goals are to establish the state of the art, to show where future progress may best be made, to highlight innovations and progress, and to build a strong collaborative community. In the CAGI experiments, participants are typically provided genetic variants and make blind predictions of resulting phenotypes.
Despite the promise of big data, little evidence has been generated for clinical practice with data driven systems. A new model for collaborative access, exploration, and analyses of integrated clinical data will be presented with a standard database, Medical Information Mart for Intensive Care - III (MIMIC III), for translational clinical research. The proposed model addresses the significant disconnect between data collection at the point of care and translational clinical research.
Intensive care units house the sickest and most technologically-dependent of patients. In these environments, patient care and therapeutic interventions occur and are carried out in real-time and the impact of technology and information access on sustaining life is essential. Yet, decision-making is not limited to what is captured within the electronic health record. Access to real-time information is essential for the front-line clinician.
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 following paper will be discussed:
The AMIA Clinical Decision Support (CDS) Working Group proposes a webinar on the Learning Health System (LHS). The LHS is defined as a system “in which progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and healthcare.” The LHS vision “aims to mobilize and empower multiple and diverse stakeholders to collaboratively realize a national-scale (and ultimately global), person-centered, continuous and rapid learning heal
This presentation will introduce CLAMP, a clinical natural language processing (NLP) toolkit that provides not only high-performance NLP modules, but also a user-friendly interface for customizing NLP pipelines. More specifically, we will introduce CLAMP-Cancer, a version that facilitates users to extract cancer information from pathology reports.
The AMIA Public Health Informatics Working Group is proud to present this webinar.