Deep learning predicts one-year mortality from ECGs, even in 'normal' cases.
problem Predicting mortality from 12-lead ECGs using deep learning.
method Deep neural network model trained on 1,775,926 ECGs, validated on 297,548 'normal' ECGs.
result Deep learning model predicts one-year mortality with AUC of 0.85, and Cox Proportional Hazard model reveals a significant hazard ratio.
Study builds machine learning models to predict ICU patients' one-year mortality.
problem Improving mortality prediction accuracy in ICU patients with heart disease.
method Developed and evaluated various machine learning models using MIMIC-III database.
result Best models achieved 85.12% prediction accuracy and AUC of 0.901.
Machine learning predicts one-year mortality with high accuracy.
problem Predicting one-year mortality for patients with life-limiting illnesses.
method Applied five machine learning techniques: SVM, KNN, Gradient Boosting, Random Forest, and MLP.
result Gradient Boosting Classifier achieved the highest AUC ROC of 0.911.
Enhanced mortality prediction model using EHR and free-text data.
problem Predicting one-year mortality in AMI patients.
method Used deep learning models with word embedding features from free-text discharge summaries.
result Significant improvement in model performance (92.89% accuracy, 0.928 F-measure).
Bounds derived for contract values in life insurance with financial market interaction.
problem Incompleteness in life tables for modern insurance products.
method Derivation of upper and lower bounds for hybrid functionals of lifetime under different assumptions.
result Characterization of worst- and best-case contract values over compatible mortality processes.
The article presents a new non-parametric approach for forecasting mortality and fertility using Gaussian process regression.
problem Precise forecasting of demographic movements in developed countries.
method Gaussian process regression with natural cubic spline and spectral mixture covariance functions.
result The approach shows significant improvements in forecasting precision and robustness.
Research creates a machine learning model for predicting TAVI patient mortality.
problem Lack of robust risk scores for TAVI patients.
method Gradient boosting on decision trees, feature analysis and selection, model validation.
result Model outperforms existing risk scores with AUC of 0.83.
The paper proposes a consensus algorithm to improve deep neural network interpretability and accuracy in mortality prediction.
problem The black-box nature and overgeneralization of deep neural networks in healthcare applications.
method An (n, k) consensus algorithm that is insensitive to adversarial examples and can reliably reject out-of-distribution samples. result The consensus algorithm improves both prediction accuracy and interpretability of deep neural network models in mortality prediction.
The paper develops stochastic models for mortality rates using infinite dimensional processes.
problem Uncertainty in demographic projections of future mortality rates.
method Forward mortality models driven by Wiener process and Poisson random measure.
result Consistency conditions for forward mortality improvements and mortality rates.
Clinical models trained on EHRs degrade in performance over time due to data drift.
problem Model performance degradation over time in clinical settings.
method Accessed year of care for each record in MIMIC, aggregated features into clinical concepts, and tested mitigation strategies.
result State-of-the-art models show significant performance drops when tested on future data compared to historical data.
Cointegration helps insurers understand long-range mortality patterns.
problem Insurers struggle to detect long-range dependence in their mortality data.
method Cointegration techniques applied to mixed fractional Brownian motion (mfBm) to capture long-range dependence.
result Cointegration brings long-range dependence information from national mortality data to insurers' models.
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.
Paper assesses how pandemic data impacts mortality models.
problem Impact of pandemic data on mortality projections.
method Calibrated Li & Lee model with transformed weekly data.
result Impact quantified, scenarios generated for future mortality.
New approach models individual vitality for better mortality predictions.
problem Limited ability of existing mortality models to capture individual complexity.
method Developed a four-component framework for individual vitality dynamics.
result Demonstrates improved analytical and practical outcomes in life insurance and lifetime decision-making.
In recent years, a market for mortality derivatives began developing as a way to handle systematic mortality risk, which is inherent in life insurance and annuity contracts. Systematic mortality risk is due to the uncertain development of future mortality intensities, or {\it hazard rates}. In this paper, we develop a …
RNN beats Lee-Carter in forecasting mortality rates.
problem Forecasting mortality rates across different demographics.
method Long Short-Term Memory (LSTM) recurrent neural network trained on multiple countries, ages, and sexes.
result RNN model outperforms the Lee-Carter model in mortality rate forecasting.
Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling…
Study historical cholera epidemics and simulate long-term mortality impacts.
problem Long-term impacts of mortality shocks on longevity.
method Historical analysis of cholera epidemics and mathematical modeling of stochastic Individual-Based models.
result Simulated long-term mortality impacts following a mortality shock.
Model predicts one-year NDVI for Four Corners region.
problem Long-term forecasting of vegetation conditions using climate attributes.
method Two-phase machine learning model using historical climate data.
result Open-source tools outperform alternative methods for NDVI forecasts.
Satellite images predict U.S. county mortality rates.
problem Predicting mortality rates in U.S. counties using satellite imagery.
method Convolutional neural network trained on crude mortality rates, learned features interpreted using Shapley Additive Feature Explanations.
result Predicted mortality from satellite images correlated strongly with true mortality rates (Pearson r=0.72).
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.
Develops a bi-variate stochastic framework to model mortality and interest rates with long-range dependence.
problem Captures long-range dependence and instantaneous correlation in mortality and interest rates.
method Mixed fractional Brownian motions, analytical solutions, risk-neutral measure, sequential parameter estimation.
result Explicit pricing of zero-coupon bonds and extreme mortality bonds, practical implications for pricing and risk management.
Study on excess mortality in Germany during 2020-21.
problem Analyzing excess mortality during the pandemic in Germany.
method Empirical study using official death counts.
result Provided conclusions for insurance businesses.
Study finds actuarial unfairness in China's pension system, proposing income-dependent annuitization rules.
problem Actuarial fairness in China's NDC pension system when mortality differs across income groups.
method Developed a mortality-differentiated Lee-Carter framework with group-specific baseline mortality schedules and a common period effect, estimated using national and subgroup data.
result Substantial actuarial unfairness in the current age-only divisor, with a reverse transfer from poorer to richer retirees.
Investigates RI strategies for life insurers with LRD mortality rates.
problem Effect of long-range dependent mortality rates on RI strategies.
method Volterra mortality model, compound Poisson process, open-loop equilibrium mean-variance criterion.
result Explicit equilibrium RI controls derived and uniqueness studied.
A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating signifi…
Proposes a new model for mortality forecasting considering age groups and cohort effects.
problem Longevity risk due to ageing population.
method Mixed-effects time-series approach with age groups dependency and random cohort effects.
result Remarkable improvements in forecast accuracy compared to the CBD model.
This paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee-Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and…
Study proposes a new model for joint survival annuity valuation.
problem Valuation of joint survival annuities and options.
method Linear-rational Wishart mortality model based on stochastic matrix affine process.
result Derives closed-form expression for joint survival annuity and option.
In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database …
Develops a GP framework for age and year-specific mortality surfaces.
problem Learning the covariance structure of age and year-specific mortality surfaces.
method Genetic programming algorithm to search for the most expressive GP kernel.
result Reveals the presence/absence of cohort effects in different populations.
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
problem Accurate prediction of all-cause in-hospital mortality in healthcare.
method Comprehensive feature engineering approach using vital signs, laboratory results, and demographic data.
result Random Forest model achieved highest performance with AUC of 0.94, significantly outperforming other models.
Study uses zero-shot models to forecast mortality rates globally.
problem Forecasting mortality rates without task-specific fine-tuning.
method Two state-of-the-art foundation models (TimesFM and CHRONOS) and traditional/machine learning methods were evaluated.
result CHRONOS outperformed traditional methods for shorter-term forecasts, but TimesFM consistently underperformed.
This study shows unstructured clinical notes can improve mortality prediction.
problem Lack of effective use of unstructured clinical notes in mortality prediction.
method Used a hierarchical architecture with convolutional and recurrent layers to predict in-hospital mortality from unprocessed clinical notes.
result Achieved higher metrics in mortality prediction compared to structured data approaches.
Optimal annuitization strategy depends on age, labor income, and mortality risk.
problem Maximizing utility from consumption and labor income under age-dependent mortality.
method Dynamic programming approach to derive closed-form solutions.
result Post-retirement labor income acts as a substitute for annuitization.
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.
Deep learning predicts ICU mortality with enhanced interpretability.
problem Improving mortality prediction accuracy and clinician trust in AI.
method Trained a deep learning model on MIMIC-III to interpret nursing notes.
result Model reaches ROC of 0.8629, outperforming SAPS-II.
Deep learning predicts SAH patient mortality from initial CT scans.
problem High mortality rates in SAH patients.
method CNN-based algorithm using transfer learning on CT scans.
result Model accurately predicts mortality (74% accuracy, 82% AUC).
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…
Two new models forecast multiple subpopulations' mortality, outperforming existing methods.
problem Modeling mortality patterns and trajectories in related populations.
method Adapted multivariate functional principal component analysis techniques.
result The second proposed model outperforms existing methods in forecast accuracy.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
In this paper, we are concerned with the valuation of Catastrophic Mortality Bonds and, in particular, we examine the case of the Swiss Re Mortality Bond 2003 as a primary example of this class of assets. This bond was the first Catastrophic Mortality Bond to be launched in the market and encapsulates the behaviour of …
The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
problem Developing tools to monitor high-risk patients during the COVID-19 pandemic.
method Data-driven random forest classification model using baseline characteristics and symptoms.
result Model predicts COVID-19 mortality with excellent performance (AUC: 0.91), identifying novel predictors.
The paper develops a method to predict ICU mortality risk across diverse patient populations.
problem Improving patient survival by recognizing risky trajectories during ICU stays.
method Domain adaptation strategies to learn mortality prediction models robust to diverse ICU populations.
result The proposed model outperforms baselines, achieving AUC numbers up to 0.88 for the Cardiac ICU population.
Study improves mortality prediction in ICU patients using feature engineering and 1D CNN.
problem Improving mortality prediction in ICU patients with high-dimensional, imbalanced, and missing data.
method Feature engineering, 1D Convolutional Neural Network (1D CNN), traditional machine learning algorithms.
result Best AUC of 0.848 achieved with 1D CNN model.
The paper analyzes optimal retirement timing considering age-dependent mortality risk.
problem Optimal retirement timing under age-dependent mortality risk.
method Formulated as a stochastic control and optimal stopping problem, transformed into a finite time horizon, three-dimensional degenerate optimal stopping problem.
result Existence of an optimal retirement boundary, characterized as a unique solution to a nonlinear integral equation.
In this paper, we detail the main simulation methods used in practice to measure one-year reserve risk, and describe the bootstrap method providing an empirical distribution of the Claims Development Result (CDR) whose variance is identical to the closed-form expression of the prediction error proposed by Wüthrich et a…