American Medical Informatics Association Annual Symposium 

About: American Medical Informatics Association Annual Symposium is an academic conference. The conference publishes majorly in the area(s): Computer science & Health care. Over the lifetime, 6225 publications have been published by the conference receiving 85035 citations.

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

Abstract: The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

STRIDE--An integrated standards-based translational research informatics platform.

Abstract: STRIDE (Stanford Translational Research Integrated Database Environment) is a research and development project at Stanford University to create a standards-based informatics platform supporting clinical and translational research STRIDE consists of three integrated components: a clinical data warehouse, based on the HL7 Reference Information Model (RIM), containing clinical information on over 13 million pediatric and adult patients cared for at Stanford University Medical Center since 1995; an application development framework for building research data management applications on the STRIDE platform and a biospecimen data management system STRIDE’s semantic model uses standardized terminologies, such as SNOMED, RxNorm, ICD and CPT, to represent important biomedical concepts and their relationships The system is in daily use at Stanford and is an important component of Stanford University’s CTSA (Clinical and Translational Science Award) Informatics Program

Evaluation of PICO as a Knowledge Representation for Clinical Questions

Abstract: The paradigm of evidence-based medicine (EBM) recommends that physicians formulate clinical questions in terms of the problem/population, intervention, comparison, and outcome. Together, these elements comprise a PICO frame. Although this framework was developed to facilitate the formulation of clinical queries, the ability of PICO structures to represent physicians’ information needs has not been empirically investigated. This paper evaluates the adequacy and suitability of PICO frames as a knowledge representation by analyzing 59 real-world primary-care clinical questions. We discovered that only two questions in our corpus contain all four PICO elements, and that 37% of questions contain both intervention and outcome. Our study reveals prevalent structural patterns for the four types of clinical questions: therapy, diagnosis, prognosis, and etiology. We found that the PICO framework is primarily centered on therapy questions, and is less suitable for representing other types of clinical information needs. Challenges in mapping natural language questions into PICO structures are also discussed. Although we point out limitations of the PICO framework, our work as a whole reaffirms its value as a tool to assist physicians practicing EBM.

SNOMED clinical terms: overview of the development process and project status.

Abstract: Two large health care reference terminologies, SNOMED RT and Clinical Terms Version 3 , are in the process of being merged to form a comprehensive new work referred to as SNOMED Clinical Terms. The College of American Pathologists and the United Kingdom s National Health Service have entered into a collaborative agreement to develop this new work. Both organizations have extensive terminology development and maintenance experience. This paper discusses the process and status of SNOMED CT development and how the resources and expertise of both organizations are being used to develop this new terminological resource. The preliminary results of the merger process, including mapping, the merger of upper levels of each hierarchy, and attribute harmonization are also discussed.

Infodemiology: tracking flu-related searches on the web for syndromic surveillance.

Abstract: Background Syndromic surveillance uses health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response. Objective While most syndromic surveillance systems rely on data from clinical encounters with health professionals, I started to explore in 2004 whether analysis of trends in Internet searches can be useful to predict outbreaks such as influenza epidemics and prospectively gathered data on Internet search trends for this purpose. Results There is an excellent correlation between the number of clicks on a keyword-triggered link in Google with epidemiological data from the flu season 2004/2005 in Canada (Pearson correlation coefficient of current week clicks with the following week influenza cases r=.91). The "Google ad sentinel method" proved to be more timely, more accurate and - with a total cost of Can$365.64 for the entire flu-season - considerably cheaper than the traditional method of reports on influenza-like illnesses observed in clinics by sentinel physicians. Conclusion Systematically collecting and analyzing health information demand data from the Internet has considerable potential to be used for syndromic surveillance. Tracking web searches on the Internet has the potential to predict population-based events relevant for public health purposes, such as real outbreaks, but may also be confounded by "epidemics of fear". Data from such "infodemiology studies" should also include longitudinal data on health information supply.