A systematic review of weight-related communication trainings for physicians: What do we know and how can we inform future development of training programs?

It is reported that physicians lack training to address weight-related concerns with patients. To overcome this, training programs have been implemented in medical settings to prepare physicians to have conversations with patients. However, it is unclear the degree of consistency among existing training programs and factors associated with better outcomes. The objective of this study was to systematically review the existing literature in this area to determine differences in content, outcomes, and implementation of existing studies that test weight-related communication training programs for physicians. A systematic literature review of online databases including PubMed, PsycINFO, and Proquest was conducted with the assistance of a librarian. Search terms included: health communication, training, physician training, weight, and obesity. Studies were selected based on the following inclusion criteria: physicians are post-graduate medical doctors; trainings encompassed weight-related communication; and outcomes were tied to physician uptake of skills, knowledge, and self-efficacy, or patient-related outcomes. Two coders reviewed studies using detailed inclusion criteria. Disagreements were resolved by consensus among authors. Half of studies assessed outcomes in both patients and physicians. Trainings including motivational interviewing (MI) assessing patient outcomes found increases in patient knowledge, satisfaction, motivation, and weight loss, respectively. Whereas, non-MI trainings assessing patient outcomes found an increase in patient weight loss, confidence and motivation, or no changes in patient outcomes. This review was the first to examine programs aimed to teach physicians to communicate with patients about weight. Future studies should examine the effect of physician communication on BMI.https://scholarscompass.vcu.edu/gradposters/1025/thumbnail.jp

Training a perceptron in a discrete weight space

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-line learning with discrete/continuous transfer functions and off-line Hebb learning. The generalization error of the clipped weights decays asymptotically as $exp(-K \alpha^2)$/$exp(-e^{|\lambda| \alpha})$ in the case of on-line learning with binary/continuous activation functions, respectively, where $\alpha$ is the number of examples divided by N, the size of the input vector and $K$ is a positive constant that decays linearly with 1/L. For finite $N$ and $L$, a perfect agreement between the discrete student and the teacher is obtained for $\alpha \propto \sqrt{L \ln(NL)}$. A crossover to the generalization error $\propto 1/\alpha$, characterized continuous weights with binary output, is obtained for synaptic depth $L > O(\sqrt{N})$.Comment: 10 pages, 5 figs., submitted to PR

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice