Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
48 results for “health economics”
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
problem Evaluating the predictive value of macroeconomic indicators for public health targets.
method Examined multiple forecasting approaches including neural networks, generalized additive models, random forests, and time series models with exogenous indicators.
result Macroeconomic indicators provide consistent and reproducible predictive signals for health workforce and infrastructure measures, but less so for other targets.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…
Algorithm optimizes lockdown policies balancing health and economy.
problem Balancing health and economic impacts of lockdowns during pandemics.
method Reinforcement learning to automatically compute lockdown policies.
result Algorithm learns optimal lockdown policies from disease and population data.
In complex systems, many different parts interact in non-obvious ways. Traditional research focuses on a few or a single aspect of the problem so as to analyze it with the tools available. To get a better insight of phenomena that emerge from complex interactions, we need instruments that can analyze simultaneously com…
Deep RL solves complex economic models with heterogeneous agents.
problem Solving models with heterogeneous economic actors is difficult.
method Reinforcement Learning techniques for solving general equilibrium models.
result Successfully captures economic behaviors induced by age-based health risks.
Paper proposes a new estimator for nested expectations with faster convergence.
problem Estimating nested expectations is computationally challenging.
method Nested kernel quadrature estimators with proof of faster convergence rate.
result The proposed method requires fewer samples for accurate estimation.
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…
ESOP uses Bayesian optimization to find optimal lock-down schedules.
problem Finding optimal lock-down schedules balancing health and economy.
method Bayesian optimization interacting with epidemiological models.
result ESOP schedules balance public health and economic impacts.
The paper develops a method to optimize individualized treatment rules for cost-effectiveness.
problem Developing cost-effective individualized treatment rules for healthcare policy.
method Using conditional random forest and net-monetary-benefit (NMB) to estimate optimal CE-ITR.
result The approach optimizes healthcare resource allocation by maximizing health gains and minimizing costs.
The paper analyzes optimal timing for converting wealth into annuities in the presence of a mortality shock.
problem Optimal timing for converting retirement wealth into annuities under a mortality risk.
method Derives explicit closed-form solutions for value function and optimal stopping boundaries.
result The optimal strategy depends on the interplay between annuity attractiveness, investment returns, and bequest motives.
Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated pictur…
Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large data…
This study examines how economic policy uncertainty impacts commodity prices across different crises.
problem Impact of economic policy uncertainty on commodity prices during various crises.
method Wavelet coherence analysis of time series data.
result Commodity prices are more correlated during global financial and Covid-19 crises.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
The global financial crisis, beginning in 2008, took an historic toll on national economies around the world. Following equity market crashes, unemployment rates rose significantly in many countries: Italy was among those. What will be the impact of such large shocks on Italian healthcare finances? An empirical model f…
Research predicts healthcare index movements using historical OHLC data.
problem Predicting the directional movement of healthcare indices based on historical data.
method Supervised classification task with a one-step-ahead rolling window, using a diverse feature set including OHLC ratios.
result Robust predictive performance with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6, highlighting the importance of nowcasting features.
Study examines how COVID-19 affects bond yields using network filtering methods.
problem Impact of COVID-19 on sovereign bond yields.
method Network filtering methods applied to a correlation matrix of sovereign bond yields.
result Mean correlation decreases across all filtering methods during the COVID-19 period.
Among American women, the rate of breast cancer is only second to lung cancer. An estimated 12.4% women will develop breast cancer over the course of their lifetime. The widespread use of social media across the socio-economic spectrum offers unparalleled ways to facilitate information sharing, in particular as it pert…
The paper reviews methods for estimating individual treatment effects using non-parametric regression models.
problem Estimating heterogeneous treatment effects in observational data.
method Non-parametric regression models to estimate individual treatment effects.
result A review and development of existing state-of-the-art frameworks for individual treatment effects estimation.
Labor market institutions are central for modern economies, and their polices can directly affect unemployment rates and economic growth. At the individual level, unemployment often has a detrimental impact on people's well-being and health. At the national level, high employment is one of the central goals of any econ…
Deep learning text embeddings improve fraud detection in healthcare insurance.
problem Improving fraud detection in healthcare insurance claims.
method Proposed deep learning architectures for text embeddings.
result Our approach outperforms other methods in detecting fraudulent claims.
This paper introduces a novel framework for designing fair and sustainable unemployment benefits, grounded in cooperative game theory and real-time fiscal policy. The labor market is modeled as a coalitional game, where a random subset of participants is employed, generating stochastic economic output. To ensure fairne…
The distribution of firms' growth and firms' sizes is a topic under intense scrutiny. In this paper we show that a thermodynamic model based on the Maximum Entropy Principle, with dynamical prior information, can be constructed that adequately describes the dynamics and distribution of firms' growth. Our theoretical fr…
Bayesian model for cost-effectiveness analysis with subgroup discovery.
problem Statistical challenges in cost-effectiveness analysis, especially with non-random treatment assignment and censored data.
method Developed a nonparametric Bayesian model using Dirichlet and Gamma processes to estimate cost-survival distributions and identify cost-effectiveness subgroups.
result Identified and estimated policy-relevant causal CEA estimands using a Bayesian nonparametric g-computation procedure.
Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repe…
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …
AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariatescs.LG
AICov integrates population covariates for better COVID-19 forecasting.
problem Forecasting COVID-19 with broader social context.
method Integrative deep learning framework with LSTM and multiple data sources.
result Improved prediction of COVID-19 cases and deaths with population risk factors.
Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencodercs.LG
2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactorial disorder with a complex aetiology. The genetic determinants remain largely elusive, with only a handful of identified candidate genes. G…
Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisationstat.AP
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly…
Study shows data breaches cause significant financial losses for firms, especially in health sector.
problem Understanding the economic impact of cyber incidents on listed firms.
method Event study using abnormal returns over 2012-2022, adjusting for event-induced variance and residual cross-correlation.
result Data breaches cause significant financial losses for firms, especially in health sector.
Repository tackles fake health news in cancer research.
problem Spread of fake health news over the internet.
method Developed comprehensive FakeHealth repository with rich features and detailed explanations.
result Repository helps in understanding and validating health fake news datasets.
Study uses machine learning to predict future health from various health data types.
problem Predicting future health using diverse health data types.
method Applied machine learning (neural networks and XGBoost) to longitudinal data from 6830 individuals.
result Health-related measures were the strongest predictors of future health status, while genetic data performed poorly.
Study user engagement in mobile health apps for health workers in resource-poor settings.
problem Detect churn and tailor content for health workers in mobile health apps.
method Probabilistic and survival analysis of behavioral logs.
result Personalized measures of meaningful engagement can enhance health outcomes.
Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experimentecon.EM
New insights show Medicaid impacts on ED use vary widely, with some groups seeing significant increases.
problem Understanding the varied impacts of Medicaid on emergency department use.
method Causal machine learning methods to identify heterogeneous impacts.
result Meaningful heterogeneity in the effect of Medicaid on ED use, with a small group driving the overall effect.
New algorithm forecasts health indicators for better equipment lifespan prediction.
problem Improving equipment lifespan prediction through health indicator forecasting.
method Generative + scenario matching approach using Gaussian Process.
result Superior performance compared to existing methods.
New method provides valid confidence intervals for spatial associations.
problem Limited insight into covariate-response relationships in spatial settings.
method Lipschitz-driven uncertainty quantification for spatial association.
result Valid frequentist confidence intervals for associations in spatial settings.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
LHIEM model predicts health, income, and employment over years.
problem Lack of path dependency in health policy simulations.
method Discrete-time microsimulation with Markov chain modules.
result Validates health care financing proposal through detailed modeling.
Automatically assesses the quality of online health articles.
problem Lack of automated tools to evaluate the quality of online health information.
method Data mining approach using 10 quality criteria and feature selection.
result Classifier achieved 84%-90% accuracy on 10 criteria.
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision he…
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Study assesses health plan risk measures for Solvency Capital Requirement.
problem Assessing risk measures for health plans to meet Solvency Capital Requirement.
method Three-part regression model with three GLMs for claim counts, episode allocation, and severity.
result Reduction in regression models compared to traditional methods.
Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…
Deep neural network predicts health costs better than traditional models.
problem Accurate prediction of healthcare costs for optimal cost management.
method Developed a deep neural network to predict future health care costs from health insurance claims records.
result Deep neural network outperformed ridge regression and Morbi-RSA models in cost prediction.
Examines fairness in ML for health, highlighting its importance and challenges.
problem Ensuring fairness in ML models for health to prevent health disparities.
method Reviews fairness notions in ML for health, including group, individual, and causal-based approaches.
result Discusses the importance and challenges of fairness in health-focused ML applications.
HealthSyn generates synthetic user behavior data for health interventions.
problem Lack of representative data for testing AI health interventions.
method Uses Markov processes to simulate diverse user actions, generating logs for ML algorithms.
result Synthetic data can be used to develop, test, and evaluate ML algorithms and RL-based interventions.
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators…