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Purpose Radiology reports often contain follow-up imaging recommendations. However, these recommendations are not always followed up by referring physicians and patients. Failure to comply in a timely manner can lead to delayed treatment, poor patient outcomes, unnecessary testing, lost revenue, and legal liability. Therefore, the primary objective of this research was to determine adherence rates to follow-up recommendations. Methods We extracted radiology examination–related data, including report text, for examinations performed between January 1, 2010, and February 28, 2017, from the radiology information system at an academic institution. The data set contained 2,972,164 examinations. The first 6 years were used as the period during which a follow-up recommendation was to be detected, allowing for a maximum of 14 months for a follow-up examination to be performed. Results At least one recommendation for follow-up imaging was present in 10.6% of radiology reports. Overall, the follow-up imaging adherence rate was 58.14%. Mammography had the highest follow-up adherence rate at 69.03%, followed by MRI at 67.54%. Of the modalities, nuclear medicine had the lowest adherence rate at 37.93%. Conclusions This study confirms that follow-up imaging adherence rates are inherently low and vary by modality and that appropriate interventions may be needed to improve compliance to follow-up imaging recommendations.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology reports to determine the most likely follow-up examination for a preceding recommendation. Pairwise inter-annotator F-score ranged from 0.853 to 0.868; the corresponding F-score of the classifier in identifying follow-up exams was 0.807. Our study describes a methodology to automatically determine the most likely follow-up exam after a follow-up imaging recommendation. The accuracy of the algorithm suggests that automated methods can be integrated into a follow-up management application to improve adherence to follow-up imaging recommendations. Radiology administrators could use such a system to monitor follow-up compliance rates and proactively send reminders to primary care providers and/or patients to improve adherence.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations.Radiology-related examination data, including report text, for examinations performed between June 1, 2015, and July 31, 2017, were extracted from the radiology departments at the University of Washington (UW) and Lahey Hospital and Medical Center (LHMC). The UW dataset contained 923,885 examinations, and the LHMC dataset contained 763,059 examinations. A 1-year period was used for detection of imaging recommendations and up to 14-months for the follow-up examination to be performed.On the basis of an algorithm with 97.9% detection accuracy, the follow-up imaging recommendation rate was 11.4% at UW and 20.9% at LHMC. Excluding mammography examinations, the overall follow-up imaging adherence rate was 51.9% at UW (range, 44.4% for nuclear medicine to 63.0% for MRI) and 52.0% at LHMC (range, 30.1% for fluoroscopy to 63.2% for ultrasound) using a matcher algorithm with 76.5% accuracy.This study suggests that follow-up imaging adherence rates vary by modality and between sites. Adherence rates can be influenced by various legitimate factors. Having the capability to identify patients who can benefit from patient engagement initiatives is important to improve overall adherence rates. Monitoring of follow-up adherence rates over time and critical evaluation of variation in recommendation patterns across the practice can inform measures to standardize and help mitigate risk.

This paper presents the development of a surrogate modeling technique for efficient non-planar fatigue crack growth analysis in mechanical components under multi-axial loading. Non-planar crack fronts are freely deformable space curves and require a high-dimensional representation. The large number of Cartesian co-ordinate variables involved in crack front representation makes it prohibitively expensive to train surrogate models for crack growth. Therefore, in our previous work, the crack shape was approximated using a planar parametrized representation. However, the parametrized representation limits the choice of crack shapes that can be considered. This paper presents the development of a non-parametric crack shape representation that allows for construction of a surrogate model for non-planar crack growth with complex crack shapes. The surrogate model is trained using a few runs of high-fidelity 3D simulations and predicts the evolution of a non-planar crack front under a given multi-axial, variable amplitude load history. We first parametrize the crack fronts as 3D spline curves with a fixed number of nodes. Instead of modeling the crack growth in this high dimensional data space, we project the data to a lower dimensional space using Principal Component Analysis (PCA) and then model the crack growth in this lower dimensional space. Finally, the predicted crack fronts are recovered using PCA back to the original data space. The proposed crack representation, growth modeling and recovery are illustrated using training points gathered from high-fidelity 3-D finite element simulations of non-planar crack growth in a cylindrical component similar to a rotorcraft mast, and the ability of the surrogate model to accurately predict the evolution of the crack growth over entire load histories is demonstrated.

Mechanical components subject to multiaxial variable amplitude loading may experience non-planar crack growth, which usually requires three dimensional finite element analyses for high fidelity simulation of non-planar fatigue crack growth. This is computationally expensive and prohibits recurrent use of high fidelity models in further probabilistic life prediction analysis. This paper presents an efficient and accurate two-stage approach for planar approximation of non-planar crack growth that reduces the computational effort. In this method, the non-planar crack is first approximated using an equivalent planar crack. Then, using the equivalent representation, planar crack growth analyses designed to account for uncertainty in crack growth are conducted. The proposed methodology employs two surrogate models: the first surrogate model is trained using 3-D simulations of non-planar fatigue crack growth to capture the relationship between the applied load history and equivalent planar crack orientation. The second surrogate model is trained using planar crack growth simulation to calculate the stress intensity factor as a function of crack size, crack orientation, and load magnitude for use in planar crack growth analysis. Individual predictions of the two surrogate models, as well as their combined predictions are verified for accuracy using full 3-D finite element simulations. The verified two-stage approach is then demonstrated in an illustrative example of uncertainty quantification in 3-D crack growth prediction in a mechanical component similar to a rotorcraft mast.

accuracy in terms of discretization error. Further discussed are the challenges that a design-optimization setting poses to the estimation of discretization error and how the ‘optimum’ mesh-refinement assessment is, in fact, nested within the design-optimization problem. The paper puts forth two significant contributions for multidisciplinary design-optimization formulations: 1) investigation of the impact of the so-called design inputs to discretization error in multidisciplinary design optimization, and 2) development of a concurrent optimization framework for simultaneous mesh refinement and design parameter optimization for multidisciplinary systems. The proposed method is illustrated using a simplified aircraft wing-design problem. I. Introduction I

This paper proposesmethods to estimate the discretization error in the system output of coupledmultidisciplinary simulations. In such systems, the governing equations for each discipline are numerically solved by a different computational code, and each discipline has different mesh size parameters. A classic example of multidisciplinary analysis involves fluid–structure interaction, where the element sizes in fluid and structure meshes are typically different. The general case of three-dimensional steady-state problems is considered in the current paper, where mesh refinement is possible in all three spatial directions for each discipline. Two aspects of discretization error, which are of interest in multidisciplinary analysis, are considered: disciplinary mesh sizes and the mismatch of disciplinary meshes at the interface at which boundary conditions are exchanged. Two alternate representations for the discretization error for the previously specified generic case are presented: 1) ignoring mesh mismatch at the interface and 2) considering mesh mismatch at the interface. Polynomial, rational function, and Gaussian process error models are used to represent the discretization error. The proposed error models are illustrated using a threedimensional fluid–structure interaction problem of an aircraft wing.

e18862Background: We present a system for automated estimation of diagnostic concordance between radiology and pathology reports, with a focus on prostate. Such a system can play a critical monitor...

One of the most fundamental challenges in predictive modeling and simulation involving materials is quantifying and minimizing the errors that originate from the use of approximate constitutive laws (with uncertain parameters and/or model form). We propose to use functional derivatives of the quantity of interest (QoI) with respect to the input constitutive laws to quantify how the QoI depends on the entire input functions as opposed to its parameters as is common practice. This functional sensitivity can be used to (i) quantify the prediction uncertainty originating from uncertainties in the input functions; (ii) compute a first-order correction to the QoI when a more accurate constitutive law becomes available, and (iii) rank possible high-fidelity simulations in terms of the expected reduction in the error of the predicted QoI. We demonstrate the proposed approach with two examples involving solid mechanics where linear elasticity is used as the low-fidelity constitutive law and a materials model including non-linearities is used as the high-fidelity law. These examples show that functional uncertainty quantification not only provides an exact correction to the coarse prediction if the high-fidelity model is completely known but also a high-accuracy estimate of the correction with only a few evaluations of the high-fidelity model. The proposed approach is generally applicable and we foresee it will be useful to determine where and when high-fidelity information is required in predictive simulations.

The optimal solution of a design optimization problem is dependent on the predictive models used to evaluate the objective and constraints. Since different models give different predictions and can yield different design decisions, when more than one model is available, the choice of model used to represent the objectives/constraints of the design becomes important. This paper addresses the problem of model selection among physics-based models during the prediction stage, which is in contrast to model selection during the calibration and validation stages, and therefore affects design under uncertainty. Model selection during calibration addresses the problem of selecting the model that is likely to provide the best generalization of the calibration data over the entire domain. Model selection during the validation stage examines the validity of a calibrated model by comparing its predictions against the validation data. This paper presents an approach that allows for model selection during the prediction stage, which selects the “best” model for each prediction point. The proposed approach is based on the estimation of the model prediction error under stationary/nonstationary uncertainty. By selecting the best model at each prediction point, the proposed approach partitions the input domain of the models into nonoverlapping regions. The effects of measurement noise, sparseness of validation data, and model prediction uncertainty are included in deriving a probabilistic selection criterion for model selection. The effects of these uncertainties on the classification errors are analyzed. The proposed approach is demonstrated for the problem of selecting between two parametric models for energy dissipation in a mechanical lap joint under dynamic loading, and for the problem of selecting fatigue crack growth models.

Discretization error estimation in the system output of multidisciplinary simulations, where each disciplinary simulation has multidirectional mesh refinement, is considered in this paper. In such systems, the governing equations for each discipline are numerically solved by a different computational code, and each discipline has different mesh size parameters. The general case of three-dimensional steady state problems is considered in the current paper. Two aspects of discretization error, that are of interest in multidisciplinary analysis, are considered: disciplinary mesh sizes, and the mismatch of disciplinary meshes at the interface at which boundary conditions are exchanged. Two alternate representations for discretization error for the above specified generic case are presented: (1) ignoring mesh mismatch at the interface, and (2) considering mesh mismatch at the interface. Polynomial, rational function, and Gaussian process error models are used to represent the discretization error. The proposed error models are illustrated using a three-dimensional fluid-structure interaction problem of an aircraft wing using ANSYS multifield module.

Observation of spatially distributed oceanographic phenomena using sensor-enabled AUVs involves a trade-off between coverage and resolution. In this paper the performance of adaptive variation sensitive sample distributions in such a sensing task is evaluated under mission constraints such as finite measurement time and finite vehicle speed and compared to uniform sampling. The relative performance of the four algorithms considered is characterized in terms of localization of features in the test functions.

This work focuses on the observation of environmental phenomena that occur as spatial distributions in two and three dimensions, using sensor-enabled mobile vehicle (ground,air or undersea). Algorithms to guide an adaptive exploration of a given region through systematic choice of sampling locations under the constraints imposed by vehicles are presented. Variation sensitive multiresolution sample distributions are achieved through an iterative variation sensitive estimation of the unknown process.

Algorithms for sensor deployment and adaptive sampling form the basis for multisensor fusion of spatio-temporal data from a wireless environmental network of deployed sensors. Derivation of sampling algorithms based on parametric methods are described. These algorithms form the basis for deployment of an array of wireless CTD (conductivity, temperature, depth) sensors to observe basic oceanographic data in Tampa Bay, Florida, USA. This distributed sensor network communicates using RF wireless 802.11b systems, and provides data in real-time to a shore observation station. In the experiments described here, five CTD sensors recorded reliable data over 25 hours. These data have been analyzed using multisensor fusion algorithms to characterize the temporal and spatial patterns. The resulting data analysis is available for integration with other observations made during these experiments, including biological and chemical variables. The approach demonstrates the ability to design and deploy a distributed sensor network that monitors real-time spatio-temporal oceanographic data, and supports further deployments that will incorporate mobile nodes capable of adaptive reconfiguration.