Automated annotation and classification of BI-RADS assessment from radiology reports.

The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports.

A Taxonomy of Delivery and Documentation Deviations During Delivery of High-Fidelity Simulations.

Introduction: We developed a taxonomy of simulation delivery and documentation deviations noted during a multicenter, high-fidelity simulation trial that was conducted to assess practicing physicians' performance. Eight simulation centers sought to implement standardized scenarios over 2 years. Rules, guidelines, and detailed scenario scripts were established to facilitate reproducible scenario delivery; however, pilot trials revealed deviations from those rubrics.

A Prototype for a Hybrid System to Support Systematic Review Teams: A Case Study of Organ Transplantation.

We describe a prototype for a hybrid system designed to reduce the number of citations needed to re-screen (NNRS) by systematic reviewers, where citations include titles, abstracts, and metadata. The system obviates the need for screening the entire set of citations a second time, which is typically done to control human error. The reference set is based on a complex review about organ transplantation (N=10,796 citations).

Building a gold standard to construct search filters: a case study with biomarkers for oral cancer.

Objective: To support clinical researchers, librarians and informationists may need search filters for particular tasks. Development of filters typically depends on a "gold standard" dataset. This paper describes generalizable methods for creating a gold standard to support future filter development and evaluation using oral squamous cell carcinoma (OSCC) as a case study.

Feature engineering and a proposed decision-support system for systematic reviewers of medical evidence.

Objectives: Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies.

Are dentists interested in the oral-systemic disease connection? A qualitative study of an online community of 450 practitioners.

Background: Dentists in the US see an increasing number of patients with systemic conditions. These patients are challenging to care for when the relationship between oral and systemic disease is not well understood. The prevalence of professional isolation exacerbates the problem due to the difficulty in finding expert advice or peer support.

Comparative effectiveness research designs: an analysis of terms and coverage in Medical Subject Headings (MeSH) and Emtree.

Objectives: We analyzed the extent to which comparative effectiveness research (CER) organizations share terms for designs, analyzed coverage of CER designs in Medical Subject Headings (MeSH) and Emtree, and explored whether scientists use CER design terms.

Methods: We developed local terminologies (LTs) and a CER design terminology by extracting terms in documents from five organizations. We defined coverage as the distribution over match type in MeSH and Emtree.

Screening nonrandomized studies for medical systematic reviews: a comparative study of classifiers.

Objectives: To investigate whether (1) machine learning classifiers can help identify nonrandomized studies eligible for full-text screening by systematic reviewers; (2) classifier performance varies with optimization; and (3) the number of citations to screen can be reduced.

Methods: We used an open-source, data-mining suite to process and classify biomedical citations that point to mostly nonrandomized studies from 2 systematic reviews. We built training and test sets for citation portions and compared classifier performance by considering the value of indexing, various feature sets, and optimization.

Using natural language processing to enable in-depth analysis of clinical messages posted to an Internet mailing list: a feasibility study.

Background: An Internet mailing list may be characterized as a virtual community of practice that serves as an information hub with easy access to expert advice and opportunities for social networking. We are interested in mining messages posted to a list for dental practitioners to identify clinical topics. Once we understand the topical domain, we can study dentists' real information needs and the nature of their shared expertise, and can avoid delivering useless content at the point of care in future informatics applications.

Barriers to implementing evidence-based clinical guidelines: a survey of early adopters.

Objective: The purpose of this study was to identify barriers that early-adopting dentists perceive as common and challenging when implementing recommendations from evidence-based (EB) clinical guidelines.

Method: This is a cross-sectional study. Dentists who attended the 2008 Evidence-based Dentistry Champion Conference were eligible for inclusion.

Towards automating the initial screening phase of a systematic review.

Systematic review authors synthesize research to guide clinicians in their practice of evidence-based medicine. Teammates independently identify provisionally eligible studies by reading the same set of hundreds and sometimes thousands of citations during an initial screening phase. We investigated whether supervised machine learning methods can potentially reduce their workload.

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information.

High-soluble-fiber foods in conjunction with a telephone-based, personalized behavior change support service result in favorable changes in lipids and lifestyles after 7 weeks.

Objective: To evaluate whether an intervention of foods high in soluble fiber from psyllium and/or oats plus a telephone-based, personalized behavior change support service improves serum lipids and elicits cholesterol-managing lifestyle changes vs usual care.

Design: 7-week randomized, controlled intervention.

Subjects/setting: 150 moderately hypercholesterolemic men and women, age range 25 to 70 years.