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The computer-assisted resuscitation algorithm (CARA) is the software component of an automatic infusion pump system being designed by US Army’s Walter Reed Institute of Research (WRAIR) to be used in the battlefields of tomorrow. Such medical devices are safety critical, and their use needs to be approved by the US Food and Drug Administration (FDA) – a process on which formal methods can have a great impact. In this special section, six papers on the analysis of CARA’s requirements are presented. In the rest of this introduction, we present the framework and summary of results from those papers. [ABSTRACT FROM AUTHOR]

Task Force on 'Clinical Algorithms for Fracture Risk' commissioned by the American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee has recommended that FRAX® models in the US do not include adjustment for race and ethnicity. This position paper finds that an agnostic model would unfairly discriminate against the Black, Asian and Hispanic communities and recommends the retention of ethnic and race-specific FRAX models for the US, preferably with updated data on fracture and death hazards. In contrast, the use of intervention thresholds based on a fixed bone mineral density unfairly discriminates against the Black, Asian and Hispanic communities in the US. This position of the Working Group on Epidemiology and Quality of Life of the International Osteoporosis Foundation (IOF) is endorsed both by the IOF and the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO). [ABSTRACT FROM AUTHOR]

Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an "emergent subject," a subject that is made visible through algorithmic techniques (2013). Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems. [ABSTRACT FROM AUTHOR]

Presents reflections on the Principles of Distributed Computing (PODC) conferences in the United States. Memories of how it all started; Relationship between formal modeling and the algorithmic research that forms the core of PODC; Old distributed computing problems will require new solutions for new settings.

The standard Diamond-Mortensen-Pissarides (DMP) labor search model generates low volatility in labor market variables relative to average labor productivity, the so-called Shimer puzzle. Hagedorn and Manovskii (Am Econ Rev 98(4):1692-1706, 2008) demonstrate that recalibrating the standard DMP model to be consistent with the small vacancy posting cost and wage elasticity observed in the data can resolve the Shimer puzzle. They close by stating that their calibration strategy would also resolve the Shimer puzzle in the real business cycle (RBC) search framework. In this paper, we examine their claim and find that their strategy resolves the Shimer puzzle in the RBC search model for linear preferences (with risk neutrality and infinite Frisch elasticity of labor supply), but falls significantly short for more standard assumptions on the degree of relative risk aversion (of 1-2) and Frisch elasticity (of 2-3), in line with empirical estimates. While our conclusions are based on highly accurate solutions using the Generalized Stochastic Simulation Algorithm, we also assess the accuracy of a frequently used linearization method and its implications for the assessment of labor market volatility. [ABSTRACT FROM AUTHOR]

There has been much recent interest in synthesis algorithms that generate finite state machines from scenarios of intended system behavior. One of the uses of such algorithms is in the transition from requirements scenarios to design. Despite much theoretical work on the nature of these algorithms, there has been very little work on applying the algorithms to practical applications. In this paper, we apply the Whittle&Schumann synthesis algorithm [32] to a component of an air traffic advisory system under development at NASA Ames Research Center. We not only apply the algorithm to generate state machine designs from scenarios but also show how to generate code from the generated state machines using existing commercial code generation tools. The results demonstrate the possibility of generating application code directly from scenarios of system behavior. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR), one of the major and long-term microvascular complications of diabetes, is the most common cause of vision loss and blindness in the working population of the world. Even with the management of diabetes, most patients will develop some forms of DR after approximately 20 years. However, DR is a treatable disease throughout the disease progression. To provide appropriate DR management, the USA and European countries have successfully implemented systematic early DR screening programs. At the same time, some computer-aided DR screening systems, which combine advanced DR detection algorithms and telemedicine technology, have also been developed for early-stage DR detection. Some of them have been tested in the DR screening programs. In this paper, we focus on a review of the major automatic DR screening systems which have performed large-scale evaluation rather than give an extensive review of all published DR grading algorithms. We first present the structures of the automatic systems and their supporting algorithms developed by the research groups, as well as the practices of the systems in their screening programs. We further present a more detailed review of the DR lesion detection algorithms in each system and reveal how the DR screening systems successfully practiced in clinical trials or large-scale screening programs. We also review recently new research areas as well as deep learning-based DR screening systems and compare them with the traditional lesion detection-based DR screening systems. The performances of the systems in the trials are summarized by considering the specificity and sensitivity with respect to the scale of testing datasets. At last, we will discuss future challenges. [ABSTRACT FROM AUTHOR]

Summary: The Forteo Patient Registry (FPR) aims to estimate the incidence of osteosarcoma in US patients treated with teriparatide. Enrollment began in 2009 and will continue through 2019, with linkage planned through 2024. To date, no incident cases of osteosarcoma have been identified among patients registered in the FPR.Introduction: The Forteo Patient Registry (FPR) was established in 2009 to estimate the incidence of osteosarcoma in US patients treated with teriparatide. The objective of this paper is to describe study methods, challenges encountered, and progress to date.Methods: The FPR is a prospective US registry designed to link data from participants annually with state cancer registries. Patient enrollment is planned for 10 years (2009-2019) and annual linkage with US state cancer registries for 15 years (2010-2024). All US state cancer registries and DC were invited to participate. Patients are recruited using pre-enrollment materials included in teriparatide device packaging, kits, and brochures distributed by health-care providers; a toll-free number; and a study website. A linkage algorithm is used to match data from enrolled participants with cancer registry data.Results: For the eighth annual linkage in 2017, information necessary for linkage with 63,270 patients in the FPR was submitted to each of the 42 participating registries. These patients contributed approximately 242,782 person-years of follow-up. A total of 5268 adult osteosarcoma cases diagnosed since January 1, 2009, were available for linkage from participating state cancer registries. To date, no incident cases of osteosarcoma have been identified among patients registered in the FPR.Conclusions: Based on the estimated 242,782 person-years of observation as of the eighth annual linkage and projecting current enrollment rate to study end in 2024, it is anticipated that the completed study will be able to detect a fourfold increase in the risk of osteosarcoma if one exists. [ABSTRACT FROM AUTHOR]

6–11-year-old children provide a critical window for physical activity (PA) interventions. The Virtual Fitness Buddy ecosystem is a precision health PA intervention for children integrating mixed reality technology to connect people and devices. A cluster randomized, controlled trial was conducted across 19 afterschool sites over two 6-month cohorts to test its efficacy in increasing PA and decreasing sedentary behavior. In the treatment group, a custom virtual dog via a mixed reality kiosk helped children set PA goals while sharing progress with parents to receive feedback and support. Children in the control group set PA goals using a computer without support from the virtual dog or parents. 303 children had 8+ hours of PA data on at least one day of each of the 3 intervention time intervals. Conversion of sedentary time was primarily to light-intensity PA and was strongest for children with low baseline moderate-to-vigorous PA than children above 45 min of baseline moderate-to-vigorous PA. Findings suggest that the VFB ecosystem can promote sustainable PA in children and may be rapidly diffused for widespread public health impact. [ABSTRACT FROM AUTHOR]

For evaluating the contents of trucks, containers, cargo, and passenger vehicles by a non-intrusive gamma-ray or X-ray imaging system to determine the possible presence of contraband, three-dimensional (3D) measurements could provide more information than just 2D measurements. In this paper, a linear pushbroom scanning model is built for such a commonly used gamma-ray or X-ray cargo inspection system. Three-dimensional (3D) measurements of the objects inside a cargo can be obtained by effectively constructing a pushbroom stereo system using two such scanning systems with different scanning angles. A simple but robust calibration method is proposed to find the important parameters of the linear pushbroom sensors. Then, a fast stereo matching algorithm is developed to obtain 3D measurements of the objects under inspection. This algorithm is fully automatic based on free-form deformable registration. An interactive user interface is designed for 3D visualization of the objects of interest. Using the interactive tool, the automatic algorithm is also compared with a very simple semi-automatic algorithm based on point correlation. Experimental results of sensor calibration, stereo matching, 3D measurements and visualization of a 3D cargo container, and the objects inside, are presented. [ABSTRACT FROM AUTHOR]

Extraction of sequences of events from news and other documents based on the publication times of these documents has been shown to be extremely effective in tracking past events. This paper addresses the issue of constructing an optimal information preserving decomposition of the time period associated with a given document set, i.e., a decomposition with the smallest number of subintervals, subject to no loss of information. We introduce the notion of the compressed interval decomposition, where each subinterval consists of consecutive time points having identical information content. We define optimality, and show that any optimal information preserving decomposition of the time period is a refinement of the compressed interval decomposition. We define several special classes of measure functions (functions that measure the prevalence of keywords in the document set and assign them numeric values), based on their effect on the information computed as document sets are combined. We give algorithms, appropriate for different classes of measure functions, for computing an optimal information preserving decomposition of a given document set. We studied the effectiveness of these algorithms by computing several compressed interval and information preserving decompositions for a subset of the Reuters-21578 document set. The experiments support the obvious conclusion that the temporal information gleaned from a document set is strongly dependent on the measure function used and on other user-defined parameters. [ABSTRACT FROM AUTHOR]

In large disaggregated input-output tables the conventional practice of triangularizied hierarchy of industries may not always work. Very often there are sets of industries which are mutually related by backward and forward linkages such that they represent coherent groups that are unrelated to the remaining industries in terms of transactions taking place in the input-output tables. To identify such coherent sets or blocks of industries which we call 'industrial complexes' in the input-output tables has been the major objective of this paper. An algorithm is designed to meet this objective that also avoids the irrelevance and complications of the multivariate analyses that are usually applied to such a search. Some empirical tests are then performed to discover the industrial complexes from the input-output tables of Canada, 1961 and 1966, and the US, 1963. [ABSTRACT FROM AUTHOR]

Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap. [ABSTRACT FROM AUTHOR]

The increasing use of wearable actimetry devices in cohort studies can provide a deep and objective insight in physical activity (PA) patterns. For reliable and reproducible pattern recognition, and to minimize the influence of specific device characteristics, there is a need for a generic method to identify relevant PA events in sensor data sets on the basis of comprehensive features such as PA duration and intensity. The objectives of this paper are to present a method to identify universal event detection thresholds for such parameters, and to attempt to find stable meta-clusters of PA behaviour. PA events of 5, 10, 20 and 30 min with low, medium and high intensity thresholds found in literature and intensity deciles were computed for a random sample ( N = 100) of the NHANES 2005-06 accelerometer data set ( N = 7457). On the basis of all combinations of the above, activity events were detected, and parameters mean duration, mean intensity and event regularity were computed. Results were clustered using x-Means clustering and visualized for 5-, 10-, 20-, and 30-min events. Stable clustering results are obtained with intensity thresholds up to the 8th decile and for event durations up to 10 min. Two stable meta-clusters were detected: 'irregularly active' (intensity at 52nd percentile) and 'regularly active' (intensity at 42nd percentile). Distinct generic thresholds could be identified and are proposed. They may prove useful for further investigations of similar actimetry data sets, minimising the influence of specific device characteristics. The results also confirm that distinct PA event patterns - including event regularity - can be identified using wearable sensor devices, especially when regarding low-intensity, short-term activities which do not correspond to current PA recommendations. Further research is necessary to evaluate actual associations between sensor-based PA parameters and health outcome. The author identified generic intensity and duration thresholds for analysing objective PA data from wearable devices. This may contribute to further analyses of PA patterns along with their relations with health outcome parameters. [ABSTRACT FROM AUTHOR]

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the "GREAT PLEA" ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice. [ABSTRACT FROM AUTHOR]

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a new emerging technology in remote sensing which generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Over the last years, many algorithms have been developed with the purpose of finding endmembers, assumed to be pure spectral signatures in remotely sensed hyperspectral data sets. One of the most popular techniques has been the pixel purity index (PPI). This algorithm is very time-consuming. The reconfigurability, compact size, and high computational power of Field programmable gate arrays (FPGAs) make them particularly attractive for exploitation in remote sensing applications with (near) real-time requirements. In this paper, we present an FPGA design for implementation of the PPI algorithm. Our systolic array design includes a DMA and implements a prefetching technique to reduce the penalties due to the I/O communications. We have also included a hardware module for random number generation. The proposed method has been tested using real hyperspectral data collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite mining district in Nevada. Experimental results reveal that the proposed hardware system is easily scalable and able to provide accurate results with compact size in (near) real-time, whichmake our reconfigurable system appealing for on-board hyperspectral data processing. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology in form of three sequencing techniques for the development of alternative quarry plans using cement quarry production-sequencing algorithm. The algorithm generates multi-period quarry plans, satisfying geometric (slope) and cement plant production capacity constraints. The benefits of the approach are demonstrated through application on an existing cement manufacturing operation in Midwestern USA. [ABSTRACT FROM AUTHOR]

Clipping is the process of transforming a real valued series into a sequence of bits representing whether each data is above or below the average. In this paper, we argue that clipping is a useful and flexible transformation for the exploratory analysis of large time dependent data sets. We demonstrate how time series stored as bits can be very efficiently compressed and manipulated and that, under some assumptions, the discriminatory power with clipped series is asymptotically equivalent to that achieved with the raw data. Unlike other transformations, clipped series can be compared directly to the raw data series. We show that this means we can form a tight lower bounding metric for Euclidean and Dynamic Time Warping distance and hence efficiently query by content. Clipped data can be used in conjunction with a host of algorithms and statistical tests that naturally follow from the binary nature of the data. A series of experiments illustrate how clipped series can be used in increasingly complex ways to achieve better results than other popular representations. The usefulness of the proposed representation is demonstrated by the fact that the results with clipped data are consistently better than those achieved with a Wavelet or Discrete Fourier Transformation at the same compression ratio for both clustering and query by content. The flexibility of the representation is shown by the fact that we can take advantage of a variable Run Length Encoding of clipped series to define an approximation of the Kolmogorov complexity and hence perform Kolmogorov based clustering. [ABSTRACT FROM AUTHOR]

In general in an organisation whose system of governance involves weighted voting, a member's weight in terms of the number of votes and the formal power it represents differ. Power indices provide a means of analysing this difference. The paper uses new algorithms for computing power indices for large games. Three analyses are carried out: (1) the distribution of Banzhaf voting power among members in 1999: the results show that the United States has considerably more power over ordinary decisions than its weight of 17% but that the use of special supermajorities limits its power: (2) the effect of varying the majority requirement on the power of the IMF to act and the powers of members to prevent and initiate action (Coleman indices); the results show the effect of supermajorities severely limits the power to act and therefore renders the voting system ineffective in democratic terms, also the sovereignty of the United States within the IMF is effectively limited to just the power of veto: (3) the paper proposes the determination of the weights instrumentally by means of an iterative algorithm to give the required power distribution: this would be a useful procedure for determining appropriate changes in weights consequent on changes to individual countries' quotas; this is applied to the 1999 data. Policy recommendations are, first, that the IMF use only simple majority voting, and discontinue using special supermajorities, and, second, allocate voting weight instrumentally using power indices. [ABSTRACT FROM AUTHOR]

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) in US hospitals are costly to staff, generate about 70 % of a hospital's revenues, and operate at a staffed-capacity utilization of 60-70 %. Many hospitals allocate blocks of OR time to individual or groups of surgeons as guaranteed allocation, who book surgeries one at a time in their blocks. The booking procedure frequently results in unused time between surgeries. Realizing that this presents an opportunity to improve OR utilization, hospitals manually reschedule surgery start times one or two days before each day of surgical operations. The purpose of rescheduling is to decrease OR staffing costs, which are determined by the number of concurrently staffed ORs. We formulate the rescheduling problem as a variant of the bin-packing problem with interrelated items, which are the surgeries performed by the same surgeon. We develop a lower bound (LB) construction algorithm and prove that the LB is at least (2/3) of the optimal staffing cost. A key feature of our approach is that we allow hospitals to have two shift lengths. Our analytical results form the basis of a branch-and-bound algorithm, which we test on data obtained from three hospitals. Experiments show that rescheduling saves significant staffing costs. [ABSTRACT FROM AUTHOR]

In this paper, early detection of schizophrenia types such as hallucination and delusion propose through the high Q-factor of RADWT in EEG signal acquired during the cognitive task of the patient. The earlier diagnose obtains from the energy distribution of the EEG signal in the high resolution via optimum tuning in dilation factor,which influences the Q-factor, redundancy and ringing in the EEG signal. The early detection of type of schizophrenia prevents the illness progression and lifelong disease. In existing clinical trial, the psych clinician diagnose only the schizophrenia disease through the standard DSM screening question and Prodromal signs checklist according to the standard of American Psychiatric Association. Furthermore, clinician tries to diagnose the disease through brain imaging and EEG signal. However, procedure in the diagnosis of Schizophrenia possible only in the acute stage, minimum after 2 years of illness progression and still sub classification of the type of schizophrenia is a challenging task. In the proposed system, we acquire EEG signal during the three conditions such as reverse counting of the number, screening questions (DSM), and eye rest state with a distance of 1-m part of the clinician and patient to analyse cognitive behaviour. From the result of 25 patients EEG, signal during cognitive task show the different sub band energy pattern in RADWT to distinguish hallucination and delusion patient exactly for 21 patients and provide 84% of accuracy in sub-classification of type schizophrenia. [ABSTRACT FROM AUTHOR]

Strengthening systems of care to meet the needs of individuals with autism spectrum disorder (ASD) is of growing importance. Administrative data provide advantages for research and planning purposes, including large sample sizes and the ability to identify enrollment in insurance coverage and service utilization of individuals with ASD. Researchers have employed varying strategies to identify individuals with ASD in administrative data. Differences in these strategies can limit the comparability of results across studies. This review describes implications of the varying strategies that have been employed to identify individuals with ASD in US claims databases, with consideration of the strengths and limitations of each approach. [ABSTRACT FROM AUTHOR]

Accurate in-vivo optical characterization of colorectal polyps is key to select the optimal treatment regimen during colonoscopy. However, reported accuracies vary widely among endoscopists. We developed a novel intelligent medical device able to seamlessly operate in real-time using conventional white light (WL) endoscopy video stream without virtual chromoendoscopy (blue light, BL). In this work, we evaluated the standalone performance of this computer-aided diagnosis device (CADx) on a prospectively acquired dataset of unaltered colonoscopy videos. An international group of endoscopists performed optical characterization of each polyp acquired in a prospective study, blinded to both histology and CADx result, by means of an online platform enabling careful video assessment. Colorectal polyps were categorized by reviewers, subdivided into 10 experts and 11 non-experts endoscopists, and by the CADx as either "adenoma" or "non-adenoma". A total of 513 polyps from 165 patients were assessed. CADx accuracy in WL was found comparable to the accuracy of expert endoscopists (CADxWL/Exp; OR 1.211 [0.766–1.915]) using histopathology as the reference standard. Moreover, CADx accuracy in WL was found superior to the accuracy of non-expert endoscopists (CADxWL/NonExp; OR 1.875 [1.191–2.953]), and CADx accuracy in BL was found comparable to it (CADxBL/CADxWL; OR 0.886 [0.612–1.282]). The proposed intelligent device shows the potential to support non-expert endoscopists in systematically reaching the performances of expert endoscopists in optical characterization. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD. The Device combines behavioral features from three distinct inputs (a caregiver questionnaire, analysis of two short home videos, and an HCP questionnaire) in a gradient boosted decision tree machine learning algorithm to produce either an ASD positive, ASD negative, or indeterminate output. This study compared Device outputs to diagnostic agreement by two or more independent specialists in a cohort of 18–72-month-olds with developmental delay concerns (425 study completers, 36% female, 29% ASD prevalence). Device output PPV for all study completers was 80.8% (95% confidence intervals (CI), 70.3%–88.8%) and NPV was 98.3% (90.6%–100%). For the 31.8% of participants who received a determinate output (ASD positive or negative) Device sensitivity was 98.4% (91.6%–100%) and specificity was 78.9% (67.6%–87.7%). The Device's indeterminate output acts as a risk control measure when inputs are insufficiently granular to make a determinate recommendation with confidence. If this risk control measure were removed, the sensitivity for all study completers would fall to 51.6% (63/122) (95% CI 42.4%, 60.8%), and specificity would fall to 18.5% (56/303) (95% CI 14.3%, 23.3%). Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants' sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources. [ABSTRACT FROM AUTHOR]

Purpose: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments (EDs). This study validates an AI algorithm to triage patients presenting with Intracranial Haemorrhage (ICH) or Acute Infarct whilst also identifying a subset of patients as Normal, with the potential to function as a rule-out test. Methods: In total, 390 CT head scans were collected from 3 institutions in the UK, US and India. Ground-truth labels were assigned by 3 FRCR consultant radiologists. AI performance, as well as the performance of 3 independent radiologists, was measured against ground-truth labels. Results: The algorithm showed AUC values of 0.988 (0.978–0.994), 0.933 (0.901–0.961) and 0.939 (0.919–0.958) for ICH, Acute Infarct and Normal, respectively. Sensitivity/specificity for ICH and Acute Infarct were 0.988/0.925 and 0.833/0.927, respectively, compared to 0.907/0.991 and 0.618/0.977 for radiologists. AI rule-out of Normal scans achieved 0.93% negative predictive value (NPV) for the removal of 54.3% of Normal cases, compared to 86.8% NPV for radiologists. Conclusion: We show our algorithm can provide effective triage of ICH and Acute Infarct to prioritise acutely unwell patients. AI can also benefit clinical accuracy, with the algorithm identifying 91.3% of radiologist false negatives for ICH and 69.1% for Acute Infarct. Rule-out of Normal scans has huge potential for workload management in busy EDs, in this case removing 27.4% of all scans with no acute findings missed. [ABSTRACT FROM AUTHOR]

This is an investigation into the design of a market-based process to replace NASA's current committee process for allocating Shuttle secondary payload resources (lockers, Watts and crew). The market-based process allocates budgets of tokens to NASA internal organizations that in turn use the budget to bid for priority for their middeck payloads. The scheduling algorithm selects payloads by priority class and maximizes the number of tokens bid to determine a manifest. The results of a number of controlled experiments show that such a system tends to allocate resources more efficiently by guiding participants to make resource and payload tradeoffs. Most participants were able to improve their position over NASA's current ranking system. Furthermore, those that are better off make large improvements while the few that do worse have relatively small losses. [ABSTRACT FROM AUTHOR]

I estimate the frequencies of interracial kin relations, an important indicator of the isolation of racial groups in the United States. I use two techniques to estimate the size and heterogeneity of extended families. First, I develop a simple model that takes account only of kinship network sizes and intermarriage levels by race. This model allows a crude estimation of the frequency of multiracial kinship networks. Second, I produce more precise empirical estimates using a new hot-deck imputation method for synthesizing kinship networks from household-level survey data (the June 1990 Current Population Survey and the 1994 General Social Survey). One in seven whites, one in three blacks, four in five Asians, and more than 19 in 20 American Indians are closely related to someone of a different racial group. Despite an intermarriage rate of about 1%, about 20% of Americans count someone from a different racial group among their kin. [ABSTRACT FROM AUTHOR]

Summary: The Forteo Patient Registry estimated the incidence of osteosarcoma in US patients treated with teriparatide and enrolled in the study between 2009 and 2019. No incident cases of osteosarcoma were identified among patients registered, and the crude incidence rate was 0 (95% confidence interval [CI], 0–10.2) cases per million person-years. Purpose: The prospective, voluntary Forteo Patient Registry was established to estimate the incidence of osteosarcoma in patients who have received treatment with teriparatide (Forteo). Methods: Information on US adults prescribed teriparatide and enrolled in the Forteo Patient Registry 2009–2019 was linked with data from participating state cancer registries annually (2010–2019) to identify incident osteosarcoma cases using a standardized linkage algorithm. Teriparatide exposure was ascertained from self-reported data that included teriparatide initiation and demographics necessary to complete linkage. Osteosarcoma cases diagnosed on or after January 1, 2009, were identified by participating state cancer registries. The crude incidence rate (IR) and standardized incidence ratio (SIR) of observed cases to the expected number of cases adjusted to the background rate (3 per million person-years) and corresponding 95% CIs for the occurrence of osteosarcoma were calculated whereby the cumulative amount of person-time observed was adjusted for mortality. Results: Data for 75,247 enrolled patients (representing 361,763 cumulative person-years) were linked to each of 42 participating state cancer registries (covering 93% of the US population), which included information on 6180 cases of osteosarcoma. No matches with incident cases of osteosarcoma following registry enrollment were found. The crude IR was 0 (95% CI, 0–10.2) cases per million person-years and the SIR was 0 (95% CI, 0–3.0). Conclusions: The ability to draw conclusions about the incidence of osteosarcoma among patients participating in the registry was limited due to the smaller than expected amount of patient follow-up time and the fact that no cases were identified. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate the role of volumetric ADC (vADC) and volumetric venous enhancement (vVE) in predicting the grade of tumor differentiation in hepatocellular carcinoma (HCC).Methods: The study population included 136 HCC patients (188 lesions) who had baseline MR imaging and histopathological report. Measurements of vVE and vADC were performed on baseline MRI. Tumors were histologically classified into low-grade and high-grade groups. The parameters between the two groups were compared using Mann-Whitney U and chi-square tests for continuous and categorical parameters, respectively. Area under receiver operating characteristic (AUROC) was calculated to investigate the accuracy of vADC and vVE. Logistic regression and multivariable Cox regression were used to unveil the potential parameters associated with high-grade HCC and patient's survival, respectively.Results: Lesions with higher vADC values and a higher absolute vADC skewness were more likely to be high grade on histopathology assessment (p = 0.001 and p = 0.0291, respectively). Also, vVE showed a trend to be higher in low-grade lesions (p = 0.079). Adjusted multivariable model including vADC, vVE, and vADC skewness could strongly predict HCC degree of differentiation (AUROC = 83%). Additionally, a higher Child-Pugh score (HR = 2.39 [p = 0.02] for score 2 and HR = 3.47 [p = 0.001] for score 3), vADC skewness (HR = 1.52, p = 0.02; per increments in skewness), and tumor volume (HR = 1.1, p = 0.001; per 100 cm3 increments) showed the highest association with patients' survival.Conclusions: vADC and vVE have the potential to accurately predict HCC differentiation. Additionally, some imaging features in combination with patients' clinical characteristics can predict patient survival.Key Points: • Volumetric functional MRI metrics can be considered as non-invasive measures for determining tumor histopathology in HCC. • Estimating patient survival based on clinical and imaging parameters can be used for modifying management approach and preventing unnecessary adverse events. [ABSTRACT FROM AUTHOR]

Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model for classifying inpatient admissions based on a patient's primary diagnosis as either emergency care or elective care and predicting urgency as a numerical value. We use supervised machine learning techniques and train the model with physician-expert judgments. Our model is accurate (96%) and has a high area under the ROC curve (>.99). We provide the first comprehensive classification and urgency categorization for inpatient emergency and elective care. This model assigns urgency values to every relevant diagnosis in the ICD catalog, and these values are easily applicable to existing hospital data. Our findings may provide a basis for policy makers to create incentives for hospitals to reduce the number of inappropriate ED admissions. [ABSTRACT FROM AUTHOR]

Introduction The purpose of this article is to present the collective experiences of six federally-funded critical congenital heart disease (CCHD) newborn screening implementation projects to assist federal and state policy makers and public health to implement CCHD screening. Methods A qualitative assessment and summary from six demonstration project grantees and other state representatives involved in the implementation of CCHD screening programs are presented in the following areas: legislation, provider and family education, screening algorithms and interpretation, data collection and quality improvement, telemedicine, home and rural births, and neonatal intensive care unit populations. Results The most common challenges to implementation include: lack of uniform legislative and statutory mandates for screening programs, lack of funding/resources, difficulty in screening algorithm interpretation, limited availability of pediatric echocardiography, and integrating data collection and reporting with existing newborn screening systems. Identified solutions include: programs should consider integrating third party insurers and other partners early in the legislative/statutory process; development of visual tools and language modification to assist in the interpretation of algorithms, training programs for adult sonographers to perform neonatal echocardiography, building upon existing newborn screening systems, and using automated data transfer mechanisms. Discussion Continued and expanded surveillance, research, prevention and education efforts are needed to inform screening programs, with an aim to reduce morbidity, mortality and other adverse consequences for individuals and families affected by CCHD. [ABSTRACT FROM AUTHOR]

The article focuses on a study which examines large-scale observational evidence of cross-cultural differences in facial behavior using advanced in computer science. Topics discussed include the automated facial coding used, the reliability of the facial action unit detection algorithms across different testing subsets, multilevel regression estimates predicting each facial action's base rate percentage.

The article looks at developments in U.S. corn cash prices from 2006 to 2011 through directed acyclic graphs. The study applied daily information from 182 markets in 7 states of the U.S. Midwest region, results revealing Iowa, Ohio and Minnesota as the states with the highest corn cash prices, and results through the Linear Non-Gaussian Acyclic Model algorithm showing Iowa as the state with the highest corn price.

This study was a replication of Mazefsky et al.'s (Journal of Autism and Developmental Disabilities 43:1236-1242, 2013) investigation among a sample of 45 high ability children and adolescents diagnosed with ASD under DSM-IV-TR. Items from the ADOS and ADI-R were mapped onto DSM-5 diagnostic criteria for ASD and SCD to determine whether participants would meet either diagnosis under DSM-5. If the ADOS were administered alone, 62% of individuals diagnosed with ASD would no longer meet criteria under DSM-5; however, when the ADI-R and ADOS scores were combined, 100% of individuals would continue to meet ASD diagnosis. The ADOS was determined to be an insufficient measure for SCD due to the small number of algorithm items measuring SCD diagnostic criteria, suggesting the development of SCD measures is required. [ABSTRACT FROM AUTHOR]

Background: Abusive head trauma (AHT) is an important cause of morbidity in infants. Identifying which well-appearing infants are at risk for AHT and need neuroimaging is challenging, and concern about radiation exposure limits the use of head CT. Availability of an MRI protocol that is highly sensitive for intracranial hemorrhage would allow for AHT screening of well-appearing infants without exposing them to radiation.Objective: To develop a screening MRI protocol to identify intracranial hemorrhage in well-appearing infants at risk for AHT.Materials and Methods: Infants enrolled in a parent study of well-appearing infants at increased risk for AHT were eligible for the current study if they underwent both head CT and conventional brain MRI. A derivation cohort of nine infants with AHT was used to identify sequences that provided the highest sensitivity for intracranial hemorrhage. A validation cohort of 78 infants including both controls with normal neuroimaging and cases with AHT was used to evaluate the accuracy of the selected sequences.Results: Three pulse sequences - axial T2, axial gradient recalled echo (GRE) and coronal T1-W inversion recovery - were 100% sensitive for intracranial hemorrhage in the derivation cohort. The same sequences were 100% sensitive (25/25) and 83% specific (44/53) for intracranial hemorrhage in the validation cohort.Conclusion: A screening MRI protocol including axial T2, axial GRE and coronal T1-W inversion recovery sequences is highly sensitive for intracranial hemorrhage and may be useful as a screening tool to differentiate well-appearing infants at risk for AHT who should undergo head CT from those who can safely be discharged without head CT. Additional research is needed to evaluate the feasibility of this approach in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Prognostic classification approaches are commonly used in clinical practice to predict health outcomes. However, there has been limited focus on use of the general approach for predicting costs. We applied a grouping algorithm designed for large-scale data sets and multiple prognostic factors to investigate whether it improves cost prediction among older Medicare beneficiaries diagnosed with prostate cancer.Methods: We analysed the linked Surveillance, Epidemiology and End Results (SEER)-Medicare data, which included data from 2000 through 2009 for men diagnosed with incident prostate cancer between 2000 and 2007. We split the survival data into two data sets (D0 and D1) of equal size. We trained the classifier of the Grouping Algorithm for Cancer Data (GACD) on D0 and tested it on D1. The prognostic factors included cancer stage, age, race and performance status proxies. We calculated the average difference between observed D1 costs and predicted D1 costs at 5 years post-diagnosis with and without the GACD.Results: The sample included 110,843 men with prostate cancer. The median age of the sample was 74 years, and 10% were African American. The average difference (mean absolute error [MAE]) per person between the real and predicted total 5-year cost was US$41,525 (MAE US$41,790; 95% confidence interval [CI] US$41,421-42,158) with the GACD and US$43,113 (MAE US$43,639; 95% CI US$43,062-44,217) without the GACD. The 5-year cost prediction without grouping resulted in a sample overestimate of US$79,544,508.Conclusion: The grouping algorithm developed for complex, large-scale data improves the prediction of 5-year costs. The prediction accuracy could be improved by utilization of a richer set of prognostic factors and refinement of categorical specifications. [ABSTRACT FROM AUTHOR]

Recent updates have been proposed to the Autism Diagnostic Observation Schedule-2 Module 4 diagnostic algorithm. This new algorithm, however, has not yet been validated in an independent sample without intellectual disability (ID). This multi-site study compared the original and revised algorithms in individuals with ASD without ID. The revised algorithm demonstrated increased sensitivity, but lower specificity in the overall sample. Estimates were highest for females, individuals with a verbal IQ below 85 or above 115, and ages 16 and older. Best practice diagnostic procedures should include the Module 4 in conjunction with other assessment tools. Balancing needs for sensitivity and specificity depending on the purpose of assessment (e.g., clinical vs. research) and demographic characteristics mentioned above will enhance its utility. [ABSTRACT FROM AUTHOR]

Cancer is the leading cause of death among Asian Americans, but screening rates are significantly lower in Asians than in non-Hispanic Whites. This study examined associations between acculturation and three types of cancer screening (colorectal, cervical, and breast), focusing on the role of health insurance and having a regular physician. A cross-sectional study of 851 Chinese, Korean, and Vietnamese Americans was conducted in Maryland. Acculturation was measured using an abridged version of the Suinn-Lew Asian Self-Identity Acculturation Scale, acculturation clusters, language preference, length of residency in the US, and age at arrival. Age, health insurance, regular physician, gender, ethnicity, income, marital status, and health status were adjusted in the multivariate analysis. Logistic regression analysis showed that various measures of acculturation were positively associated with the odds of having all cancer screenings. Those lived for more than 20 years in the US were about 2-4 times [odds ratio (OR) and 95 % confidence interval (CI) colorectal: 2.41 (1.52-3.82); cervical: 1.79 (1.07-3.01); and breast: 2.11 (1.25-3.57)] more likely than those who lived for less than 10 years to have had cancer screening. When health insurance and having a regular physician were adjusted, the associations between length of residency and colorectal cancer [OR 1.72 (1.05-2.81)] was reduced and the association between length of residency and cervical and breast cancer became no longer significant. Findings from this study provide a robust and comprehensive picture of AA cancer screening behavior. They will provide helpful information on future target groups for promoting cancer screening. [ABSTRACT FROM AUTHOR]

The purpose of this study is to see whether measures of church-based and secular social support offset the effects of functional disability on change in feelings of personal control over time. Survey data were obtained from Wave 2 and Wave 3 of a nationwide sample of older adults ( N = 583). The findings suggest that spiritual support from fellow church members offsets the effects of functional disability on change in feelings of personal control over time. In contrast, neither emotional support from fellow church members nor emotional support from secular social network members exerted a similar effect. [ABSTRACT FROM AUTHOR]

Various designs of high speed EMU (electric multiple units) operating at maximum speed of 400 km/h or above are under active development in many countries. Driving at such extreme high speed, the EMUs that are equipped with multiple power units and non articulated bogies normally experience severe level of vibration. Noting one of the primary design issues of the vertical EMU vibration, the present work tries an optimization of vehicle suspension system using ten degree of freedom analytical EMU model. The objective function for the work is consisted of the rail power spectral density (PSD), transfer function of car body, and frequency dependent weighting function which may represent human riding comfort. The transfer function is formulated using an autocorrelation of the model in frequency domain. For the enhanced reliability of the optimization work, both the US FRA (Federal Railway Administration) rail PSD and the actual rail PSD acquired from Kyeong-bu high speed rail in Korea on which the particular EMU will be driven are used. The well proven BFGS (Broyden-Fletcher-Goldfarb-Shanno) method is adopted for the optimization. The optimized design is verified using a commercial dynamic system simulation code at various operating conditions. [ABSTRACT FROM AUTHOR]

The p-median offers an alternative to centroid-based clustering algorithms for identifying unobserved categories. However, existing p-median formulations typically require data aggregation into a single proximity matrix, resulting in masked respondent heterogeneity. A proposed three-way formulation of the p-median problem explicitly considers heterogeneity by identifying groups of individual respondents that perceive similar category structures. Three proposed heuristics for the heterogeneous p-median (HPM) are developed and then illustrated in a consumer psychology context using a sample of undergraduate students who performed a sorting task of major U.S. retailers, as well as a through Monte Carlo analysis. [ABSTRACT FROM AUTHOR]

In this work the spatial distribution of the location error in wireless cellular networks operating in an urban outdoor environment is studied. We assume signal strength based positioning methods that avoid additional network hardware costs and handset modifications, while approaching FCC requirements for emergency positioning. In order to cope with the computational burden associated with the required spatial discrimination of our analysis, we developed an efficient simulator that is capable of analyzing a comprehensive class of cost efficient location algorithms based on the received signal strength measurements made periodically by the mobile terminals in GSM and UMTS systems. The simulation results show that the key factors to achieve positioning accuracy are line-of-sight and homogeneous distribution of base stations in the vicinity of the mobile terminals, leading to the conclusion that the positioning error is location dependent. This conclusion motivates a reliability analysis based on the spatial segmentation of the location process. In the same scenario we estimate the a posteriori location probabilities providing a framework for the further development of reliable methodologies for the tracking of mobile terminals. [ABSTRACT FROM AUTHOR]

Treatment regimens of patients with CTCL vary widely based on clinician preference and patient tolerance. Skin directed therapies are recommended for patients with early stage IA and IB MF, with combinations used in refractory cases. While no regimen has been proven to prolong survival in advanced stages, immunomodulatory regimens should be used initially to reduce the need for cytotoxic therapies. In more advanced stages of disease, treatment efforts should strive for palliation and improvement of quality of life. With many new therapies and strategies on the horizon, the future looks promising for CTCL patients. Unfortunately, other than allogeneic HCT, there are no potential curative therapies for CTCL. Clinical trials are currently underway to identify new therapies to improve quality of life for patients, and researchers are hard at work to identify novel pathways and genes for prognostication and as targets for therapies. Importantly, collaborative clinical trials to enhance rates of accrual need to be conducted, and improved interpretation of data via standardizing end points and response criteria should be an emphasis. Recently, the International Society for Cutaneous Lymphomas (ISCL), the United States Cutaneous Lymphoma Consortium (USCLC), and the Cutaneous Lymphoma Task Force of the European Organisation for Research and Treatment of Cancer (EORTC) met to develop consensus guidelines to facilitate collaboration on clinical trials. These proposed guidelines consist of: recommendations for standardizing general protocol design; a scoring system for assessing tumor burden in skin, lymph nodes, blood, and viscera; definition of response in skin, nodes, blood, and viscera; a composite global response score; and a definition of end points. Although these guidelines were generated by consensus panels, they have not been prospectively or retrospectively validated through analysis of large patient cohorts. [ABSTRACT FROM AUTHOR]

NASA/GEWEX (National Aeronautics and Space Administration/Global Energy and Water Cycle Experiment) Surface Radiation Budget (SRB) has released its latest radiation dataset, version 3.0. We examine the accuracy of the monthly mean global radiation in China using surface-observed radiation (SOR) data at 42 stations during the period 1984-2004. Overall comparison shows a general overestimation of satellite retrieval radiation data with a bias of 14.6 W m and a root mean square error of 25.9 W m. Differences at individual stations suggested satellite data are consistently higher than surface measurements over eastern China (110°E), but occasional underestimation occurs in Western China, especially Southwest China. Intra-annual variation analysis indicates that SRB satellite radiation can capture the annual cycle well. For trend of global radiations, there are evident discrepancies between satellite retrievals and surface measurements for both the entire period and segmental terms. For the entire period from 1984 to 2004, most stations show a positive trend based on surface measurements, while the majority of collocated pixels show a negative trend. Segmental trends demonstrated that the principal difference occurred during the first period of 1981-1994. After 1994, the two datasets change similarly. Therefore, trend analysis in terms of detecting global dimming/brightening remains very difficult as surface measurements and satellite products do not agree yet. In addition, some proposals are made towards better understanding of the bias of satellite products and to improve further the satellite retrieval algorithm with better representation of both cloud and aerosol properties. [ABSTRACT FROM AUTHOR]

We study the case where agents have preferences over ranges (intervals) of values, and we wish to elicit and aggregate these preferences. For example, consider a set of climatologist agents who are asked for their predictions for the increase in temperature between 2009 and 2100. Each climatologist submits a range, and from these ranges we must construct an aggregate range. What rule should we use for constructing the aggregate range? One issue in such settings is that an agent (climatologist) may misreport her range to make the aggregate range coincide more closely with her own (true) most-preferred range. We extend the theory of single-peaked preferences from points to ranges to obtain a rule (the median-of-ranges rule) that is strategy-proof under a condition on preferences. We then introduce and analyze a natural class of algorithms for approximately eliciting a median range from multiple agents. We also show sufficient conditions under which such an approximate elicitation algorithm still incentivizes agents to answer truthfully. Finally, we consider the possibility that ranges can be refined when the topic is more completely specified (for example, the increase in temperature on the North Pole given the failure of future climate pacts). We give a framework and algorithms for selectively specifying the topic further based on queries to agents. [ABSTRACT FROM AUTHOR]

This study represents a new application of Veterans Affairs (VA) administrative data for measuring VA system-wide performance of colorectal cancer (CRC) screening adherence among veterans at average-risk for CRC. Our new measurement has two features: it is specifically designed for average-risk populations and is applied repeatedly at the veteran level each year over the study period. Using 1997-2007 VA administrative data, we developed an algorithm that first constructed 7 independent cohorts of average-risk veterans eligible for CRC screening, one for each year from 2001 to 2007, and then appended the seven cohorts together to form 2001-2007 veteran-level panel data. Veterans in a cohort for a given year were considered adherent if they received fecal occult blood test (FOBT) during that given year, or received flexible sigmoidoscopy (FS), double-contrast barium enema (DCBE), or colonoscopy during that given year or the 4 previous years. The main analysis shows that VA CRC screening rates increased from 30.11% in 2001 to 35.51% in 2004, but declined to 31.54% in 2007. Among the screened, the proportion adherent to colonoscopy increased over the 7-year period while the proportion adherent to FOBT, FS, or DCBE decreased during the same period. Sensitivity analyses, including use of a 10-year retrospective window for determining the screening adherence of colonoscopy, show the robustness of the main analysis. This new algorithm demonstrates that VA administrative data may be used for assessing VA performance of CRC screening adherence of average-risk veterans. Furthermore, our panel data may enhance understanding of factors associated with CRC screening adherence. [ABSTRACT FROM AUTHOR]