A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning

An Integrated Data Mining Approach to Real-time Clinical Monitoring and Deterioration Warning

Clinical study found that early detection and intervention are essential for preventing clinical deterioration in patients, for patients both in intensive care units (ICU) as well as in general wards but under real-time data sensing (RDS). In this paper, we develop an integrated data mining approach to give early deterioration warnings for patients under real-time monitoring in ICU and RDS. Existing work on mining real-time clinical data often focus on certain single vital sign and specific disease. In this paper, we consider an integrated data mining approach for general sudden deterioration warning. We synthesize a large feature set that includes first and second order time-series features, detrended fluctuation analysis (DFA), spectral analysis, approximative entropy, and cross-signal features. We then systematically apply and evaluate a series of established data mining methods, including forward feature selection, linear and nonlinear classification algorithms, and exploratory undersampling for class imbalance. An extensive empirical study is conducted on real patient data collected between 2001 and 2008 from a variety of ICUs. Results show the benefit of each of the proposed techniques, and the final integrated approach significantly improves the prediction quality. The proposed clinical warning system is currently under integration with the electronic medical record system at Barnes-Jewish Hospital in preparation for a clinical trial. This work represents a promising step toward general early clinical warning which has the potential to significantly improve the quality of patient care in hospitals

Tax Incidence

This chapter reviews the concepts, methods, and results of studies that analyze the incidence of taxes. The purpose of such studies is to determine how the burden of a particular tax is allocated among consumers through higher product prices, workers through a lower wage rate, or other factors of production through lower rates of return to those factors. The methods might involve simple partial equilibrium models, analytical general equilibrium models, or computable general equilibrium models. We review partial equilibrium models, where the burden of a tax is shown to depend on the elasticity of supply relative to the elasticity of demand. In particular, we consider partial equilibrium models with imperfect competition. Turning to a general equilibrium setting, we review the classic model of Harberger (1962) and illustrate its generality by applying it to a number of different contexts. We also use this model to demonstrate the practicality of analytical general equilibrium modeling through the use of log linearization techniques. We then turn to dynamic models to show how a tax on capital affects capital accumulation, future wage rates, and overall burdens. Such models might also provide analytical results or computational results. We also focus on relatively recent models that calculate the lifetime incidence of taxes, with both intratemporal and intertemporal redistribution. Finally, the chapter reviews the use of incidence methods and results in the policy process.

A survey of generative adversarial networks for synthesizing structured electronic health records

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning

Trade Policy and Export Performance in Morocco

Morocco’s trade policy is at a cross-roads. Historically, the country has had a very restrictive import regime that generated substantial transfers to domestic producers. In terms of the simple average of most-favored nation tariffs, Morocco is one of the ten most highly protected markets in the world. Yet, with the signing of the Euro-Med Agreement with the European Union and its implementation since 2000, a decision for the gradual opening of the domestic market through preferential trade liberalization was taken. This choice was subsequently reaffirmed through the conclusion of further free trade agreements with the United States and Turkey. The resulting shift in trade policy paradigms promises to create new opportunities for export-led economic growth and employment generation, while requiring adjustment of domestic producers to the new, more competitive economic environment and additional policy reforms to complement the market opening strategy.Trade, tariffs, services, logistics, export diversification, regional integration, world markets

Local Industrial Conditions and Entrepreneurship: How Much of the Spatial Distribution Can We Explain?

Why are some places more entrepreneurial than others? We use Census Bureau data to study local determinants of manufacturing startups across cities and industries. Demographics have limited explanatory power. Overall levels of local customers and suppliers are only modestly important, but new entrants seem particularly drawn to areas with many smaller suppliers, as suggested by Chinitz (1961). Abundant workers in relevant occupations also strongly predict entry. These forces plus city and industry fixed effects explain between sixty and eighty percent of manufacturing entry. We use spatial distributions of natural cost advantages to address partially endogeneity concerns.Entrepreneurship, Industrial Organization, Agglomeration, Labor Markets, Input-Output Flows, Innovation, Research and Development, Patents.

Data Mining Techniques for Complex User-Generated Data

Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains