Sequential anomaly detection in the presence of noise and limited feedback

This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: 1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations and 2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family. Moreover, the regret against static distributions coincides with the minimax value of the corresponding online strongly convex game. We also prove bounds on the number of mistakes made during the hedging step relative to the best offline choice of the threshold with access to all estimated beliefs and feedback signals. We validate the theory on synthetic data drawn from a time-varying distribution over binary vectors of high dimensionality, as well as on the Enron email dataset. © 1963-2012 IEEE.

Nonlinear parameter retrieval from three- and four-wave mixing in metamaterials

We present a generalized nonlinear susceptibility retrieval method for metamaterials based on transfer matrices and valid in the nondepleted pump approximation. We construct a general formalism to describe the transfer matrix method for nonlinear media and apply it to the processes of three- and four-wave mixing. The accuracy of this approach is verified via finite element simulations. The method is then reversed to give a set of equations for retrieving the nonlinear susceptibility. Finally, we apply the proposed retrieval operation to a three-wave mixing transmission experiment performed on a varactor loaded split ring resonator metamaterial sample and find quantitative agreement with an analytical effective medium theory model. © 2010 The American Physical Society.

Risk factors for and estimated incidence of community-associated Clostridium difficile infection, North Carolina, USA.

We determined estimated incidence of and risk factors for community-associated Clostridium difficile infection (CA-CDI) among patients treated at 6 North Carolina hospitals. CA-CDI case-patients were defined as adults (>18 years of age) with a positive stool test result for C. difficile toxin and no hospitalization within the prior 8 weeks. CA-CDI incidence was 21 and 46 per 100,000 person-years in Veterans Affairs (VA) outpatients and Durham County populations, respectively. VA case-patients were more likely than controls to have received antimicrobial drugs (adjusted odds ratio [aOR] 17.8, 95% confidence interval [CI] 6.6-48] and to have had a recent outpatient visit (aOR 5.1, 95% CI 1.5-17.9). County case-patients were more likely than controls to have received antimicrobial drugs (aOR 9.1, 95% CI 2.9-28.9), to have gastroesophageal reflux disease (aOR 11.2, 95% CI 1.9-64.2), and to have cardiac failure (aOR 3.8, 95% CI 1.1-13.7). Risk factors for CA-CDI overlap with those for healthcare-associated infection.

Cost of innovation in the pharmaceutical industry.

The research and development costs of 93 randomly selected new chemical entities (NCEs) were obtained from a survey of 12 U.S.-owned pharmaceutical firms. These data were used to estimate the pre-tax average cost of new drug development. The costs of abandoned NCEs were linked to the costs of NCEs that obtained marketing approval. For base case parameter values, the estimated out-of-pocket cost per approved NCE is $114 million (1987 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a 9% discount rate yielded an average cost estimate of $231 million (1987 dollars).

Effect of primary care physicians' use of estimated glomerular filtration rate on the timing of their subspecialty referral decisions.

BACKGROUND: Primary care providers' suboptimal recognition of the severity of chronic kidney disease (CKD) may contribute to untimely referrals of patients with CKD to subspecialty care. It is unknown whether U.S. primary care physicians' use of estimated glomerular filtration rate (eGFR) rather than serum creatinine to estimate CKD severity could improve the timeliness of their subspecialty referral decisions. METHODS: We conducted a cross-sectional study of 154 United States primary care physicians to assess the effect of use of eGFR (versus creatinine) on the timing of their subspecialty referrals. Primary care physicians completed a questionnaire featuring questions regarding a hypothetical White or African American patient with progressing CKD. We asked primary care physicians to identify the serum creatinine and eGFR levels at which they would recommend patients like the hypothetical patient be referred for subspecialty evaluation. We assessed significant improvement in the timing [from eGFR < 30 to ≥ 30 mL/min/1.73m(2)) of their recommended referrals based on their use of creatinine versus eGFR. RESULTS: Primary care physicians recommended subspecialty referrals later (CKD more advanced) when using creatinine versus eGFR to assess kidney function [median eGFR 32 versus 55 mL/min/1.73m(2), p < 0.001]. Forty percent of primary care physicians significantly improved the timing of their referrals when basing their recommendations on eGFR. Improved timing occurred more frequently among primary care physicians practicing in academic (versus non-academic) practices or presented with White (versus African American) hypothetical patients [adjusted percentage(95% CI): 70% (45-87) versus 37% (reference) and 57% (39-73) versus 25% (reference), respectively, both p ≤ 0.01). CONCLUSIONS: Primary care physicians recommended subspecialty referrals earlier when using eGFR (versus creatinine) to assess kidney function. Enhanced use of eGFR by primary care physicians' could lead to more timely subspecialty care and improved clinical outcomes for patients with CKD.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.

Implementation of automated reporting of estimated glomerular filtration rate among Veterans Affairs laboratories: a retrospective study.

BACKGROUND: Automated reporting of estimated glomerular filtration rate (eGFR) is a recent advance in laboratory information technology (IT) that generates a measure of kidney function with chemistry laboratory results to aid early detection of chronic kidney disease (CKD). Because accurate diagnosis of CKD is critical to optimal medical decision-making, several clinical practice guidelines have recommended the use of automated eGFR reporting. Since its introduction, automated eGFR reporting has not been uniformly implemented by U. S. laboratories despite the growing prevalence of CKD. CKD is highly prevalent within the Veterans Health Administration (VHA), and implementation of automated eGFR reporting within this integrated healthcare system has the potential to improve care. In July 2004, the VHA adopted automated eGFR reporting through a system-wide mandate for software implementation by individual VHA laboratories. This study examines the timing of software implementation by individual VHA laboratories and factors associated with implementation. METHODS: We performed a retrospective observational study of laboratories in VHA facilities from July 2004 to September 2009. Using laboratory data, we identified the status of implementation of automated eGFR reporting for each facility and the time to actual implementation from the date the VHA adopted its policy for automated eGFR reporting. Using survey and administrative data, we assessed facility organizational characteristics associated with implementation of automated eGFR reporting via bivariate analyses. RESULTS: Of 104 VHA laboratories, 88% implemented automated eGFR reporting in existing laboratory IT systems by the end of the study period. Time to initial implementation ranged from 0.2 to 4.0 years with a median of 1.8 years. All VHA facilities with on-site dialysis units implemented the eGFR software (52%, p<0.001). Other organizational characteristics were not statistically significant. CONCLUSIONS: The VHA did not have uniform implementation of automated eGFR reporting across its facilities. Facility-level organizational characteristics were not associated with implementation, and this suggests that decisions for implementation of this software are not related to facility-level quality improvement measures. Additional studies on implementation of laboratory IT, such as automated eGFR reporting, could identify factors that are related to more timely implementation and lead to better healthcare delivery.

Associations between BMI and home, school and route environmental exposures estimated using GPS and GIS: do we see evidence of selective daily mobility bias in children?

BACKGROUND: This study examined whether objective measures of food, physical activity and built environment exposures, in home and non-home settings, contribute to children's body weight. Further, comparing GPS and GIS measures of environmental exposures along routes to and from school, we tested for evidence of selective daily mobility bias when using GPS data. METHODS: This study is a cross-sectional analysis, using objective assessments of body weight in relation to multiple environmental exposures. Data presented are from a sample of 94 school-aged children, aged 5-11 years. Children's heights and weights were measured by trained researchers, and used to calculate BMI z-scores. Participants wore a GPS device for one full week. Environmental exposures were estimated within home and school neighbourhoods, and along GIS (modelled) and GPS (actual) routes from home to school. We directly compared associations between BMI and GIS-modelled versus GPS-derived environmental exposures. The study was conducted in Mebane and Mount Airy, North Carolina, USA, in 2011. RESULTS: In adjusted regression models, greater school walkability was associated with significantly lower mean BMI. Greater home walkability was associated with increased BMI, as was greater school access to green space. Adjusted associations between BMI and route exposure characteristics were null. The use of GPS-actual route exposures did not appear to confound associations between environmental exposures and BMI in this sample. CONCLUSIONS: This study found few associations between environmental exposures in home, school and commuting domains and body weight in children. However, walkability of the school neighbourhood may be important. Of the other significant associations observed, some were in unexpected directions. Importantly, we found no evidence of selective daily mobility bias in this sample, although our study design is in need of replication in a free-living adult sample.