Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndr…
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48 results for “death risk”
This paper addresses the risk-minimization problem, with and without mortality securitization, à la Föllmer-Sondermann for a large class of equity-linked mortality contracts when no model for the death time is specified. This framework includes the situation where the correlation between the market model and the time o…
In this paper we present a numerical valuation of variable annuities with combined Guaranteed Minimum Withdrawal Benefit (GMWB) and Guaranteed Minimum Death Benefit (GMDB) under optimal policyholder behaviour solved as an optimal stochastic control problem. This product simultaneously deals with financial risk, mortali…
Modeling catastrophic deaths using EVT with a microsimulation approach to reinsurance pricingq-fin.RM
Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in [6]. We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model compa…
Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may…
FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.
problem Global optimization of non-convex objective functions over measure space.
method Introduces Fast Spawn\&Prune (FS\&P) combining CPGD with birth-death process.
result First theoretical guarantee of global convergence for discrete-time stochastic algorithms.
In this paper we study the financial repercussions of the destruction of two fully armed and operational moon-sized battle stations ("Death Stars") in a 4-year period and the dissolution of the galactic government in Star Wars. The emphasis of this work is to calibrate and simulate a model of the banking and financial …
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements,…
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithmstat.ML
New algorithm predicts lung cancer progression and mortality.
problem Predicting semi-competing risk outcomes in lung cancer.
method Neural Expectation-Maximization algorithm for multi-state outcomes.
result Estimates non-parametric baseline hazards and risk functions.
Maximizing Utility of Consumption Subject to a Constraint on the Probability of Lifetime Ruinq-fin.PM
In this note, we explicitly solve the problem of maximizing utility of consumption (until the minimum of bankruptcy and the time of death) with a constraint on the probability of lifetime ruin, which can be interpreted as a risk measure on the whole path of the wealth process.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
problem Predicting clinical outcomes from irregularly sampled EHR data with competing events.
method Neural ODE-based Recurrent Neural Networks (ODE-RNN) for flexible survival time estimation.
result SurvLatent ODE outperforms Khorana Risk scores for VTE risk prediction.
AI generates a sequence of death causes from hospital records.
problem Accurate death reporting for vital statistics and policy formulation.
method Neural machine translation models to generate causal chains, incorporating medical domain knowledge.
result Achieved 16.04 BLEU score for generating accurate causal chains.
Model predicts US COVID-19 deaths with quantile estimates.
problem Predicting US COVID-19 deaths at county level.
method Hybrid machine learning and epidemiological approach, minimizing pinball loss.
result Quantile estimates accurately forecast deaths for different forecast periods.
Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software. It is as a consequence an expensive process that can in addition suffer from geospatial discrepancies, thus severely impairing the…
Machine learning predicts mask mandates reduce COVID-19 deaths.
problem Effectiveness of mask mandates on reducing COVID-19 deaths.
method Machine learning classification algorithms applied to survey data.
result Mask mandates correlated with reduced death ratios in most counties.
Optimizes retirement income with MBGs and neural networks for longevity risk.
problem Maximizing lifetime withdrawals while managing longevity risk.
method Neural-network optimization under stochastic mortality.
result International diversification and longevity pooling improve retirement outcomes.
Study shows death ratio of COVID-19 deaths increases financial volatility.
problem Impact of COVID-19 deaths on financial markets.
method Analysis of VIX index based on daily case reports and death ratios.
result Death ratio positively influences financial volatility.
The paper audits trading filters, finding a high save-to-miss ratio.
problem Improving the efficiency and accuracy of trading filters in decentralized exchanges.
method A precision audit of filter rules against real trading data, classifying rejection events.
result Conservative save-to-miss ratio of 3.7 : 1, with wider interpretation of 14.8 : 1.
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.
Study identifies clusters of EU countries with similar young mortality patterns.
problem Identify clusters of EU countries with similar mortality patterns in young population.
method Symbolic data analysis (SDA) with age, gender, and main causes of death dimensions.
result Identified clusters of EU countries with similar mortality patterns in young population.
We propose a general method to obtain approximation of the first passage time distribution for the birth-death processes. We rely on the general properties of birth-death processes, Keilson's theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth-death …
The paper optimizes insurance purchases for financial goals.
problem Maximizing probability of achieving financial goals with insurance.
method Analyzes deferred term insurance in deterministic and stochastic frameworks, considering income, consumption, and risky investment.
result Provides optimal insurance and investment strategies for achieving financial goals.
Jointly models cause-of-death mortality rates across multiple countries and genders.
problem Modeling cause-of-death mortality rates in a multinational setting.
method Multi-Output Gaussian Processes (MOGP) with Kronecker-structured kernels and latent factors.
result Efficiently captures heterogeneity and dependence across different factor inputs.
In the framework of Embedded Value new standards, namely the MCEV norms, the latest principles published in June 2008 address the issue of market and underwriting risks measurement by using stochastic models of projection and valorization. Knowing that stochastic models particularly data-consuming, the question which c…
Study birth-death dynamics for sampling Gibbs measures with nonconvex potentials.
problem Sampling Gibbs measures with nonconvex potentials.
method Birth-death dynamics, Kullback-Leibler divergence, divergence, kernel-based approximations, -convergence of gradient flows.
result Probability density converges exponentially fast to Gibbs equilibrium measure with a universal rate.
New method evaluates personalized treatment in critical care, robust to death.
problem Truncation by death in critical care makes traditional DTR evaluation ineffective.
method Principal stratification-based approach, focusing on always-survivor value function, with a semiparametrically efficient, multiply robust estimator.
result Demonstrates robustness and efficiency of the method for personalized treatment optimization.
Study shows COVID-19 increases stock market crash risk in China.
problem Impact of COVID-19 on stock market crash risk in China.
method Estimated conditional skewness using GARCH-S model and constructed fear index from Baidu Index data.
result Conditional skewness reacts negatively to daily growth in total confirmed cases, indicating increased crash risk.
We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. Parameter families for mortality trends can be chosen freely. As model settings become high dimensional, Markov chain Monte Carlo (MCMC) is used for parameter estimation. We then link our propose…
Bessel-like birth-death processphysics.soc-ph
We consider models of the population or opinion dynamics which result in the non-linear stochastic differential equations (SDEs) exhibiting the spurious long-range memory. In this context, the correspondence between the description of the birth-death processes as the continuous-time Markov chains and the continuous SDE…
This paper proposes a market consistent valuation framework for variable annuities with guaranteed minimum accumulation benefit, death benefit and surrender benefit features. The setup is based on a hybrid model for the financial market and uses time-inhomogeneous Lévy processes as risk drivers. Further, we allow for d…
A Novel Approach to the Diagnosis of Heart Disease using Machine Learning and Deep Neural Networkscs.LG
AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.
problem Misdiagnosis of heart disease leads to unnecessary deaths.
method Developed an AI application using ML and DNN algorithms on a dataset from the Cleveland Clinic Foundation.
result DNN model achieved a 92% accuracy rate, reducing misdiagnosis.
Forecasting stock market decline and recovery post-COVID-19.
problem Analyzing exogenous risk's impact on stock markets.
method Two case studies using historical data and stochastic fluctuations.
result 85% accuracy in predicting S&P500 index decline and recovery.
Paper analyzes factors affecting COVID-19 risk in US counties.
problem Identifying factors influencing COVID-19 risk in US counties.
method Combines unsupervised (K-means clustering) and supervised learning models.
result Mean temperature, poverty, obesity, and other factors are most significant.
Study proposes managing COVID-19 without economy shutdowns.
problem Avoiding lockdowns while maintaining healthcare system capacity.
method Detailed heterogeneous epidemiological model, calibrated to data.
result Countries can avoid lockdowns if ICU beds per million > 100.
We find the optimal investment strategy for an individual who seeks to minimize one of four objectives: (1) the probability that his wealth reaches a specified ruin level {\it before} death, (2) the probability that his wealth reaches that level {\it at} death, (3) the expectation of how low his wealth drops below a sp…
Pricing equity-linked life insurance contracts with multiple risk factors by neural networksq-fin.PR
The paper uses neural networks to price complex life insurance contracts with multiple risk factors.
problem Pricing equity-linked life insurance contracts with various stochastic risk factors.
method Assuming hedging to reduce local variance, the price is expressed as a system of non-linear PDEs. Reformulated as a backward SDE with jumps, solved numerically using neural networks.
result Neural networks provide an efficient numerical solution for pricing these complex contracts.
A fundamental problem in Bayesian inference and statistical machine learning is to efficiently sample from multimodal distributions. Due to metastability, multimodal distributions are difficult to sample using standard Markov chain Monte Carlo methods. We propose a new sampling algorithm based on a birth-death mechanis…
In this paper we investigate the local risk-minimization approach for a combined financial-insurance model where there are restrictions on the information available to the insurance company. In particular we assume that, at any time, the insurance company may observe the number of deaths from a specific portfolio of in…
New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.
problem Sampling from multimodal probability distributions efficiently.
method Combines birth-death process and exploration component to accelerate sampling.
result Proves exponential asymptotic convergence under mild assumptions.
Volterra mortality model: Actuarial valuation and risk management with long-range dependenceq-fin.MF
New model incorporates long-range dependence in mortality rates for better valuation and risk management.
problem Lack of appropriate models for valuing and managing mortality securities with long-range dependence.
method Proposes a novel class of Volterra mortality models that incorporate LRD, derived in closed-form solution.
result Models provide flexibility and tractability for valuing and hedging mortality-related products.
The paper estimates personalized treatment effects in medical settings with competing risks.
problem Estimating treatment effectiveness for specific events in the presence of alternative event types.
method Meta-learners combining Cox regression or random survival forests for risk modeling and elastic net regression or random forests for direct CATE modeling.
result Compared meta-learners in multiple simulation settings, providing practical guidance for model selection.
Bayesian networks (BNs) are graphical models that are useful for representing high-dimensional probability distributions. There has been a great deal of interest in recent years in the NP-hard problem of learning the structure of a BN from observed data. Typically, one assigns a score to various structures and the sear…
Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. Following t…
GMWB Riders in a Binomial Framework - Pricing, Hedging, and Diversification of Mortality Riskq-fin.PR
We construct a binomial model for a guaranteed minimum withdrawal benefit (GMWB) rider to a variable annuity (VA) under optimal policyholder behaviour. The binomial model results in explicitly formulated perfect hedging strategies funded using only periodic fee income. We consider the separate perspectives of the insur…
Quantifying the impact of COVID-19 on the US stock market: An analysis from multi-source informationq-fin.ST
Study quantifies how COVID-19 spread affects US stock markets.
problem Impact of COVID-19 on US stock market during pandemic.
method Developed a novel temporal complex network approach using econometric and ML models.
result Local spread of COVID-19 and Google searches impact abnormal stock prices.
Kernel Cox partially linear regression: building predictive models for cancer patients' survivalstat.ML
Paper develops a method to predict cancer patient survival using molecular profiles.
problem Accurately predicting cancer patient survival with complex survival-molecular profile relationships.
method Kernel Cox partially linear regression with a novel regularized garrotized kernel machine (RegGKM) method.
result The proposed method outperforms other methods in predicting survival accuracy.
DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approachq-bio.GN
Study uses DNA methylation data to predict suicidal and non-suicidal deaths.
problem Predicting suicidal and non-suicidal deaths from DNA methylation data.
method Support Vector Machines (SVM) with dimensionality reduction using PCA and t-SNE.
result t-SNE outperforms PCA in reducing data dimensionality.