Objective-To derive and validate a predictive model and novel Emergency Medical Services (EMS) screening tool for severe sepsis (SS). Design-Retrospective cohort study.Setting-A single EMS system and an urban, public hospital.Patients-Sequential adult, non-trauma, non-arrest, at-risk, EMS-transported patients between January 1, 2011 and December 31, 2012. At-risk patients were defined as having all 3 of the following criteria present in the EMS setting: heart rate >90bpm, 2) respiratory rate >20bpm, and 3) systolic blood pressure <110mmHg. Interventions-None.Measurements and Main Results-Among 66,439 EMS encounters, 555 met criteria for analysis. Fourteen percent (n=75) of patients had SS, of which 19% (n=14) were identified by EMS clinical judgment. In-hospital mortality for patients with SS was 31% (n=23). Six EMS characteristics were found to be predictors of SS: older age, transport from nursing home, Emergency Medical Dispatch (EMD) 9-1-1 chief complaint category of "Sick Person", hot tactile temperature assessment, low systolic blood pressure, and low oxygen saturation. The final predictive model showed good discrimination in derivation and validation subgroups (AUC 0.843 and 0.820, respectively). Sensitivity of the final model was 91% in the derivation group and 78% in the validation group. At a pre-defined threshold of 2 or more points, prehospital severe sepsis (PRESS) score sensitivity was 86%.Corresponding Author: Carmen C Polito, MD, MS, Division of Pulmonary, Allergy, and Critical Care Medicine, Emory University School of Medicine, 615 Michael Street, Suite 205M, Atlanta, GA 30322, Phone: (404) 712-2970, Fax: (404) 712-2974, cpolito@emory.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Conclusions-The PRESS score is a novel EMS screening tool for SS that demonstrates a sensitivity of 86% and specificity of 47%. Additional validation is needed before this tool can be recommended for widespread clinical use. HHS Public Access

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An efficient and robust medical-image indexing procedure should be user-oriented. It is essential to index the images at the right level of description and ensure that the indexed levels match the user's interest level. This study examines 240 medical-image descriptions produced by three different groups of medical-image users (novices, intermediates, and experts) in the area of radiography. This article reports several important findings: First, the effect of domain knowledge has a significant relationship with the use of semantic image attributes in image-users' descriptions. We found that experts employ more high-level image attributes which require highreasoning or diagnostic knowledge to search for a medical image (Abstract Objects and Scenes) than do novices; novices are more likely to describe some basic objects which do not require much radiological knowledge to search for an image they need (Generic Objects) than are experts. Second, all image users in this study prefer to use image attributes of the semantic levels to represent the image that they desired to find, especially using those Received July 3, 2011; revised September 16, 2011; accepted September 16, 2011 © 2011 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.21686 specific-level and scene-related attributes. Third, image attributes generated by medical-image users can be mapped to all levels of the pyramid model that was developed to structure visual information. Therefore, the pyramid model could be considered a robust instrument for indexing medical imagery. IntroductionMedical images provide vital clinical data and are considered a powerful educational resource due to their immediate, informative, and illustrative nature. Medical images can be used by clinicians for their daily practice of medicine, such as making diagnoses, planning treatment, and monitoring responses to therapy as well as for medical education and research (Cleveland & Cleveland, 2009;Kalpathy-Cramer & Hersh, 2010;Müller, Michoux, Bandon, & Geissbuhler, 2004). Past studies have reported significant learning improvements when using medical images during classes and self-education for medical students and residents (Dawes, Vowler, Allen, & Dixon, 2004; KalpathyCramer & Hersh, 2010). A single hospital radiology department alone produced 50,000 images per day in 2006 (Müller , 2007). With the dramatic explosion of digital image collections in medicine, it is important to develop advanced techniques for effective and efficient management of this information, enabling users quick and easy access in a clinically meaningful way.Image information systems (e.g., picture archiving and communication systems) provide rapid access to digitalized film images and allow users to access medical-image databases based on combinations of a patient's identification, visit dates, and study characteristics (e.g., modality and study description) (Müller et al., 2004). However, to fulfill users' various requirements under different contexts of u...

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We demonstrate an automated, multi-level method to segment white matter brain lesions and apply it to lupus. The method makes use of local morphometric features based on multiple MR sequences, including T1-weighted, T2-weighted, and fluid attenuated inversion recovery. After preprocessing, including co-registration, brain extraction, bias correction, and intensity standardization, 49 features are calculated for each brain voxel based on local morphometry. At each level of segmentation a supervised classifier takes advantage of a different subset of the features to conservatively segment lesion voxels, passing on more difficult voxels to the next classifier. This multi-level approach allows for a fast lesion classification method with tunable trade-offs between sensitivity and specificity producing accuracy comparable to a human rater.