Design thinking for concert experiences

Thesis (DM) – Indiana University, Music, 201

Synthetic Data Sharing and Estimation of Viable Dynamic Treatment Regimes with Observational Data

Significant public demand arises for rapid data-driven scientific investigations using observational data, especially in personalized healthcare. This dissertation addresses three complementary challenges of analyzing complex observational data in biomedical research. The ethical challenge reflects regulatory policies and social norms regarding data privacy, which tend to emphasize data security at the expense of effective data sharing. This results in fragmentation and scarcity of available research data. In Chapter 2, we propose the DataSifter approach that mediates this challenge by facilitating the generation of realistic synthetic data from sensitive datasets containing static and time-varying variables. The DataSifter method relies on robust imputation methods, including missForest and an iterative imputation technique for time-varying variables using the Generalized Linear Mixed Model (GLMM) and the Random Effects-Expectation Maximization tree (RE-EM tree). Applications demonstrate that under a moderate level of obfuscation, the DataSifter guarantees sufficient per subject perturbations of time-invariant data and preserves the joint distribution and the energy of the entire data archive, which ensures high utility and analytical value of the time-varying information. This promotes accelerated innovation by enabling secure sharing among data governors and researchers. Once sensitive data can be securely shared, effective analytical tools are needed to provide viable individualized data-driven solutions. Observational data is an important data source for estimating dynamic treatment regimes (DTR) that guide personalized treatment decisions. The second natural challenge regards the viability of optimal DTR estimations, which may be affected by the observed treatment combinations that are not applicable for future patients due to clinical or economic reasons. In Chapter 3, we develop restricted Tree-based Reinforcement Learning to accommodate restrictions on feasible treatment combinations in observational studies by truncating possible treatment options based on patient history in a multi-stage multi-treatment setting. The proposed new method provides optimal treatment recommendations for patients only regarding viable treatment options and utilizes all valid observations in the dataset to avoid selection bias and improve efficiency. In addition to the structured data, unstructured data, such as free-text, or voice-note, have become an essential component in many biomedical studies based on clinical and health data rapidly, including electronic health records (EHR), providing extra patient information. The last two chapters in my dissertation (Chapter 4 and Chapter 5) expands the methods developed in the previous two projects by utilizing novel natural language processing (NLP) techniques to address the third challenge of handling unstructured data elements. In Chapter 4, we construct a text data anonymization tool, DataSifterText, which generates synthetic free-text data to protect sensitive unstructured data, such as personal health information. In Chapter 5, we propose to enhance the precision of optimal DTR estimation by acquiring additional information contained in clinical notes with information extraction (IE) techniques. Simulation studies and application on blood pressure management in intensive care units demonstrated that the IE techniques can provide extra patient information and more accurate counterfactual outcome modeling, because of the potentially enhanced sample size and a wider pool of candidate tailoring variables for optimal DTR estimation. The statistical methods presented in this thesis provides theoretical and practical solutions for privacy-aware utility-preserving large-scale data sharing and clinically meaningful optimal DTR estimation. The general theoretical formulation of the methods leads to the design of tools and direct applications that are expected to go beyond the biomedical and health analytics domains.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166113/1/zhounina_1.pd

Developing Critical Collaboration Skills in Engineering Students: Results From an Empirical Study

In highly technical organizations, work is becoming increasingly distributed; requiring practicing engineers to master virtual collaboration skills while acquiring expertise in a range of collaboration technologies. Although there has been great emphasis on developing collaboration competencies in the engineering curriculum, empirical evidence of successful strategies for distributed team settings is scarce. As an attempt to fill this gap this study investigates the impact of a scalable intervention in developing virtual collaboration skills. The intervention, based on instructional scaffolds embedded with collaboration technologies, is aimed at supporting specific processes including planning, goal setting, clarifying goals and expectations, communication, coordination and progress monitoring. A quasi-experimental design was used to evaluate the impact of the intervention on student teamwork skills. Data from 278 graduate and undergraduate engineering students participating in virtual team projects was used in the analysis. Results from the analysis are presented suggesting a statistically significant impact of the intervention on self-management skills when comparing randomly assigned teams with and without the intervention. The intervention is designed to be scalable so that it can be embedded into existing project-based courses. Our findings have important implications for the development of teamwork skills in engineering courses and provide evidence of a successful strategy that can be integrated into the existing engineering curriculum

Effects Of Web-Based Interactive Modules On Engineering Students’ Learning Motivations

The purpose of this study is to assess the impact of a newly developed modules, Interactive Web-Based Visualization Tools for Gluing Undergraduate Fuel Cell Systems Courses system (IGLU), on learning motivations of engineering students using two samples (n1=144 and n2=135) from senior engineering classes. The multivariate analysis results revealed that the participants had a significant increase in their learning motivation after the treatment with the IGLU modules. This result was cross-validated with the two samples, in which the motivation mean posttest scores are significantly higher than the mean pretest scores, systematically (Sample 1: the mean score is increased by 2.09 [.32, 3.87] points, p = .021; Sample 2: the mean score is increased by 1.38 [.14, 2.61] points, p = .029). With the use of instructional technology prevailing in current university courses, the education initiative of the IGLU system and the assessment of its impact on student learning motivation provide us information to improve the modules to serve a more diverse student body. It will greatly help the development of engineering educational curriculum. With regards to the statistical inference, it is desirable to conduct further studies with a quasi-experiment control group design to assess the program effect focusing on student learning and its associations with student learning motivations and learning styles

Fat accumulates preferentially in the right rather than the left liver lobe in non-diabetic subjects

Aims: To examine the distribution of liver fat (LFAT) in non-diabetic subjects and test whether the fat in the right as compared to the left lobe correlates better with components of the metabolic syndrome or not. Methods: In this cross sectional study, we determined LFAT by H-1-MRS in the right lobe (LFAT%(MRS)), and by MRI (LFAT%(MRI)) in four regions of interest (ROIs 1-4, two in the right and two in the left lobe) in 97 non-diabetic subjects (age range 22-74 years, BMI 18-41 kg/m(2)) and compared the accuracy of LFAT(MRI) in the different ROIs in diagnosing non-alcoholic fatty liver disease (NAFLD) using areas under the receiver operator characteristic (AUROC) curves. Results: 38% of the subjects had NAFLD (LFAT%(MRS)). LFAT%(MRI) was significantly higher in the right (5.7 +/- 0.5%) than the left (5.1 +/- 0.4%) lobe (p <0.02). The AUROC for LFAT%(MRI) in the right lobe for diagnosing NAFLD was significantly better than that in the left lobe. The relationships between several metabolic parameters and LFAT%(MRI) in the left lobe were significantly worse than those for LFAT%(MRS) while there was no difference between LFAT%(MRS) and right lobe ROIs. Conclusions: Liver right lobe contains more fat and correlates better with components of the metabolic syndrome than the left in non-diabetic subjects. (C) 2017 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.Peer reviewe

Status of the Local Enforcement of Energy Efficiency Standards and Labeling Program in China

As part of its commitment to promoting and improving the local enforcement of appliance energy efficiency standards and labeling, the China National Institute of Standardization (CNIS) launched the National and Local Enforcement of Energy Efficiency Standards and Labeling project on August 14, 2009. The project’s short-term goal is to expand the effort to improve enforcement of standards and labeling requirements to the entire country within three years, with a long-term goal of perfecting overall enforcement. For this project, Jiangsu, Shandong, Sichuan and Shanghai were selected as pilot locations. This report provides information on the local enforcement project’s recent background, activities and results as well as comparison to previous rounds of check-testing in 2006 and 2007. In addition, the report also offers evaluation on the achievement and weaknesses in the local enforcement scheme and recommendations. The results demonstrate both improvement and some backsliding. Enforcement schemes are in place in all target cities and applicable national standards and regulations were followed as the basis for local check testing. Check testing results show in general high labeling compliance across regions with 100% compliance for five products, including full compliance for all three products tested in Jiangsu province and two out of three products tested in Shandong province. Program results also identified key weaknesses in labeling compliance in Sichuan as well as in the efficiency standards compliance levels for small and medium three-phase asynchronous motors and self-ballasted fluorescent lamps. For example, compliance for the same product ranged from as low as 40% to 100% with mixed results for products that had been tested in previous rounds. For refrigerators, in particular, the efficiency standards compliance rate exhibited a wider range of 50% to 100%, and the average rate across all tested models also dropped from 96% in 2007 to 63%, possibly due to the implementation of newly strengthened efficiency standards in 2009. Areas for improvement include: Greater awareness at the local level to ensure that all manufacturers register their products with the label certification project and to minimize their resistance to inspections; improvement of the product sampling methodology to include representative testing of both large and small manufacturers and greater standardization of testing tools and procedures; and continued improvement in local enforcement efforts