Exercise levels and preferences in cancer patients: a cross-sectional study

Background: Despite the benefits related to physical exercise, large numbers of cancer patients are not sufficiently active. Methods: To investigate exercise levels and preferences in cancer patients, a cross-sectional study was conducted on a random sample of 392 cancer outpatients who anonymously completed a questionnaire investigating general and medical characteristics, and expressed willingness to participate in exercise programs. Current exercise levels were estimated with the Leisure Score Index (LSI). Results: Most patients (93%) were insufficiently active but 80% declared an interest in exercise programs. Patients preferred oncologist-instructed programs and specified particular exercise needs. Multivariate logistic regression showed that willingness to exercise was associated with education (OR: 1.87; 95% CI: 1.15-3.04 beyond age 14 years vs. up to 14 years) and current physical activity (OR: 1.92; 95% CI: 1.92-3.63 for sweat-inducing activity >2 times/week vs. <1 time/week). Patients given chemotherapy were less inclined to exercise (OR: 0.45; 95% CI: 0.23-0.86) than those who did not. LSI was lower if cancer stage was advanced (β: -0.36; 95% CI: -0.75 to -0.02) than if it was in remission. High LSI was also associated with longer education, lower BMI, and longer time after diagnosis. Conclusion: Cancer patients are insufficiently active but are willing to participate in personalized exercise programs. Information from this survey may help in designing personalized interventions so these patients will achieve sufficient exercise

Enabling gradient-based optimization methods in problems with unreliable or absent derivatives

In this thesis, we focus on problems in which the derivative of the objective function is either unavailable or unreliable, which can occur in a variety of situations including the presence of legacy codes (codes written in the past but not maintained), problems of parameter tuning for simulation or optimization algorithms and engineering problems where the objective functions are the output of black-box simulation software. Despite the absence or the unreliability of the derivatives, our interest is in the resolution of the optimization problem using gradient-based methods, which take advantage of the rich and relevant information normally included in the gradient of the objective function. We address the lack of derivatives considering two different scenarios. In the first one, we consider smooth problems with additive noise affecting objective function evaluations. We assume that objective function evaluations can be obtained in a cheap and fast way and we focus on gradient approximation methods that use objective function evaluations to somehow filter the noise and build an estimate of the gradient. In the second scenario, we consider potentially non-smooth simulation-based optimization problems in which neither the objective function nor its (eventual) derivative have an explicit expression. Assuming the expensiveness of the evaluations of objective functions, we enable the usage of gradient-based methods by following an approach that is based on the creation of a neural network model that replaces the simulation software used for computing the objective function. In this way, the smooth function obtained with the neural network model and its gradient are considered in the optimization procedure

What drives a donor? A machine learning‐based approach for predicting responses of nonprofit direct marketing campaigns

Direct marketing campaigns are one of the main fundraising sources for nonprofit organizations and their effectiveness is crucial for the sustainability of the organizations. The response rate of these campaigns is the result of the complex interaction between several factors, such as the theme of the campaign, the month in which the campaign is launched, the history of past donations from the potential donor, as well as several other variables. This work, applied on relevant data gathered from the World Wide Fund for Nature Italian marketing department, undertakes different data mining approaches in order to predict future donors and non-donors, thus allowing for optimization in the target selection for future campaigns, reducing its overall costs. The main challenge of this research is the presence of thoroughly imbalanced classes, given the low percentage of responses per total items sent. Different techniques that tackle this problem have been applied. Their effectiveness in avoiding a biased classification, which is normally tilted in favor of the most populated class, will be highlighted. Finally, this work shows and compares the classification results obtained with the combination of sampling techniques and Decision Trees, ensemble methods, and Artificial Neural Networks. The testing approach follows a walk-forward validation procedure, which simulates a production environment and reveals the ability to accurately classify each future campaign

A mixed finite differences scheme for gradient approximation

In this paper, we focus on the linear functionals defining an approximate version of the gradient of a function. These functionals are often used when dealing with optimization problems where the computation of the gradient of the objective function is costly or the objective function values are affected by some noise. These functionals have been recently considered to estimate the gradient of the objective function by the expected value of the function variations in the space of directions. The expected value is then approximated by a sample average over a proper (random) choice of sample directions in the domain of integration. In this way, the approximation error is characterized by statistical properties of the sample average estimate, typically its variance. Therefore, while useful and attractive bounds for the error variance can be expressed in terms of the number of function evaluations, nothing can be said on the error of a single experiment that could be quite large. This work instead is aimed at deriving an approximation scheme for linear functionals approximating the gradient, whose error of approximation can be characterized by a deterministic point of view in the case of noise-free data. The previously mentioned linear functionals are no longer considered as expected values over the space of directions, but rather as the filtered derivative of the objective function by a Gaussian kernel. By using this new approach, a gradient estimation based on a suitable linear combination of central finite differences at different step sizes is proposed and deterministic bounds that do not depend on the particular sample of points considered are computed. In the noisy setting, on the other end, the variance of the estimation error of the proposed method is showed to be strictly lower than the one of the estimation error of the Central Finite Difference scheme. Numerical experiments on a set of test functions are encouraging, showing good performances compared to those of some methods commonly used in the literature, also in the noisy setting

Computational issues in Optimization for Deep networks

In this paper, we aim at investigating some relevant computational issues, in particular the role of global vs local optimizers, the role of the choice of the starting point, as well as of the optimization hyper-parameters setting on classification performances of deep networks. We carry out extensive computational experiments using nine different open-source optimization algorithms to train a deep CNN on an image classification task. Eventually, we also assess the role of wideness and depth on the computational optimization performance, carrying out additional tests with wider and deeper neural architecture

Data of patients undergoing rehabilitation programs

In this data article, we present a dataset made up of personal, social and clinical records related to patients undergoing a rehabilitation program. Data refers to records registered in the "Acceptance/Discharge Report for the rehabilitation area” (ADR) which implements the Italian law (DGR 731/2005) and refer to hospitalization at the rehabilitation hospital of Rome "San Raffaele" in the years from 2015 to 2018 of patients suffering from orthopedic and neurological pathologies. For each ADR report, the clinical status of the patient at the date of acceptance and discharge is reported using, among other, the Barthel index as a measure of the Activities Daily Living of the patient. These data can be used to understand the influence of many different factors in the rehabilitation progress of clinical patients