Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

The Effects of Growth Regulators and Apical Bud Removal on Growth, Flowering, and Corms Production of Two Gladiolus Varieties

Gladiolus is commonly propagated from corms. The multiplication rate of corms is low and to increase the propagation rate, we examined a combination of apical bud removal and the application of growth regulators. The experiments were conducted in two varieties, ‘Rose Supreme’ and ‘White Prosperity’, and over two seasons. The apical buds on the planting corms were either removed or left intact before the same corms were soaked in a suspension with either 100 ppm of benzyladenine (BA), 100 ppm of gibberellic acid (GA3), or pure water. The results showed that apical bud removal increased the number of corms and shoots. GA3 had limited the effect on corm and shoot production, but instead resulted in increased total leaf area and leaf weight per shoot. BA, on the other hand, increased the number of corms and shoots. Overall, the removal of the apical bud plus application of BA increased the number of corms and shoots but reduced the average corm diameter and leaf weight per shoot. This was clearer in ‘Rose Supreme’ than in ‘White Prosperity’. To maximize flower production for the coming season, farmers need to produce a high number of planting corms, but they also need to balance this with a sufficient corm size and the production of flowers of good quality. The application of growth regulators in combination with apical bud removal should be fine-tuned to avoid a situation that leads to the production of too many small or too few large corms.publishedVersio

Lessons for non-VA care delivery systems from the U.S. Department of Veterans Affairs Quality Enhancement Research Initiative: QUERI Series

The U.S. Veterans Health Administration (VHA) may have a very different structure and function from the organizations and practices that provide medical care to most Americans, but those organizations and practices could learn a lot from the VHA's Quality Enhancement Research Initiative (QUERI). There are at least six topics of increasing importance for implementation research where QUERI experience should be of value to other non-VHA organizations, both within and external to the United States: 1) Researcher-clinical leader partnerships for care improvement; 2) Attention to culture, capacity, leadership, and a supportive infrastructure; 3) Practical economic evaluation of quality implementation efforts; 4) Human subject protection problems; 5) Sustainability of improvements; and 6) Scale-up and spread of improvements

Colorado school counseling investments payoff for students!: a CCD Center case study

Coalition for Career Development Centerhttps://irp.cdn-website.com/81ac0dbc/files/uploaded/CO-paper-Final.pd

Support varieties for selfinjective algebras

Support varieties for any finite dimensional algebra over a field were introduced by Snashall-Solberg using graded subalgebras of the Hochschild cohomology. We mainly study these varieties for selfinjective algebras under appropriate finite generation hypotheses. Then many of the standard results from the theory of support varieties for finite groups generalize to this situation. In particular, the complexity of the module equals the dimension of its corresponding variety, all closed homogeneous varieties occur as the variety of some module, the variety of an indecomposable module is connected, periodic modules are lines and for symmetric algebras a generalization of Webb's theorem is true

EM algorithm for Bayesian estimation of genomic breeding values

<p>Abstract</p> <p>Background</p> <p>In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS) method, (which are called BayesA and BayesB, respectively, in some literatures), have been so far proposed for the estimation of SNP effects. However, much computational burden is imposed on the MCMC-based Bayesian methods. A method with both high computing efficiency and prediction accuracy is desired to be developed for practical use of genomic selection.</p> <p>Results</p> <p>EM algorithm applicable for BSR is described. Subsequently, we propose a new EM-based Bayesian method, called wBSR (weighted BSR), which is a modification of BSR incorporating a weight for each SNP according to the strength of its association to a trait. Simulation experiments show that the computational time is much reduced with wBSR based on EM algorithm and the accuracy in predicting GBV is improved by wBSR in comparison with BSR based on MCMC algorithm. However, the accuracy of predicted GBV with wBSR is inferior to that with SSVS based on MCMC algorithm which is currently considered to be a method of choice for genomic selection.</p> <p>Conclusions</p> <p>EM-based wBSR method proposed in this study is much advantageous over MCMC-based Bayesian methods in computational time and can predict GBV more accurately than MCMC-based BSR. Therefore, wBSR is considered a practical method for genomic selection with a large number of SNP markers.</p

The DIAMOND Initiative: Implementing Collaborative Care for Depression in 75 Primary Care Clinics

Background: The many randomized trials of the collaborative care model for improving depression in primary care have not described the implementation and maintenance of this model. This paper reports how and the degree to which collaborative care process changes were implemented and maintained for the 75 primary care clinics participating in the DIAMOND Initiative (Depression Improvement Across Minnesota–Offering a New Direction). Methods: Each clinic was trained to implement seven components of the model and participated in ongoing evaluation and facilitation activities. For this study, assessment of clinical process implementation was accomplished via completion of surveys by the physician leader and clinic manager of each clinic site at three points in time. The physician leader of each clinic completed a survey measure of the presence of various practice systems prior to and one and two years after implementation. Clinic managers also completed a survey of organizational readiness and the strategies used for implementation. Results: Survey response rates were 96% to 100%. The systems survey confirmed a very high degree of implementation (with large variation) of DIAMOND depression practice systems (mean of 24.4 ± 14.6%) present at baseline, 57.0 ± 21.0% at one year (P = \u3c0.0001), and 55.9 ± 21.3% at two years. There was a similarly large increase (and variation) in the use of various quality improvement strategies for depression (mean of 29.6 ± 28.1% at baseline, 75.1 ± 22.3% at one year (P = \u3c0.0001), and 74.6 ± 23.0% at two years. Conclusions: This study demonstrates that under the right circumstances, primary care clinics that are prepared to implement evidence-based care can do so if financial barriers are reduced, effective training and facilitation are provided, and the new design introduces the specific mental models, new care processes, and workers and expertise that are needed. Implementation was associated with a marked increase in the number of improvement strategies used, but actual care and outcomes data are needed to associate these changes with patient outcomes and patient-reported care

Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem