We provide a novel approach and an exploratory study for modelling life event choices and occurrence from a probabilistic perspective through causal discovery and survival analysis. Our approach is formulated as a bi-level problem. In the upper level, we build the life events graph, using causal discovery tools. In the…
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48 results for “life events”
Predict human lives using sequences of life events.
problem Predicting human life outcomes based on detailed event sequences.
method Adapting natural language processing techniques to analyze life events.
result Models predict diverse outcomes with high accuracy, outperforming state-of-the-art models.
Study compares resampling methods for rare event prediction in longitudinal studies.
problem Predicting rare events in longitudinal follow-up studies.
method Comparison of resampling methods to improve standard regression models.
result Effect of sampling rate on model predictive performance.
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.
Paper tackles event stream outliers using a novel weight function.
problem Learning event streams with unexpected absences or occurrences.
method Temporal point process framework with a novel weight function.
result The proposed method effectively handles both commission and omission outliers.
New methods improve insurance data quality for catastrophic events.
problem Improving precision and size of insurance data for catastrophic events.
method Bootstrap, bootknife, and GAN algorithms.
result Compared MSE and MAE of simulated outputs, direct algorithm for fuzzy expert opinion.
The study improves life insurance surrender risk modeling using various machine learning techniques.
problem Accurate modeling of surrender risk in life insurance to meet Solvency II directive requirements.
method Extensive experiments with XGBoost, random forest, GLM, and neural networks; resampling analysis; time-dependent confidence bands.
result Models trained on resampled data predict significantly biased event probabilities, highlighting the need for complementary assessments.
Point Process Flowscs.LG
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions…
Continuous-time event sequences represent discrete events occurring in continuous time. Such sequences arise frequently in real-life. Usually we expect the sequences to follow some regular pattern over time. However, sometimes these patterns may be interrupted by unexpected absence or occurrences of events. Identificat…
Estimand framework and intercurrent events handling for clinical trials with time-to-event outcomesstat.ME
Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
problem Handling intercurrent events in clinical trials with time-to-event outcomes.
method Defines estimands and six ICE handling strategies, including new competing-risk strategy.
result Novel methods for handling intercurrent events in clinical trials with time-to-event outcomes.
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventiv…
Bitcoin: A life in crisesq-fin.ST
Study examines Bitcoin's price history and identifies recurring events.
problem Understanding Bitcoin's price fluctuations and recurring events.
method Analyzed BTC price time-series (2010-2021), identified recurring events, and approximated price evolution using a Fibonacci sequence.
result BTC price history shows recurring events with similar duration and can be approximated using a Fibonacci sequence.
New algorithm predicts spatio-temporal events with improved accuracy.
problem Non-stationary spatio-temporal prediction on dense and sparse sequences.
method Probabilistic approach using point processes and self-organizing decision trees.
result Significant performance improvements over baseline and state-of-the-art methods.
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweetscs.CL
Crowdsourcing and active learning reduce manual annotation in social media event classification.
problem Manual annotation is time-consuming and resource-intensive in social media event classification.
method Crowdsourcing pipeline combined with active learning strategies.
result Active learning strategies help reduce the number of tweets needed for classification.
Proposes a new framework to disentangle event influences in MTPP.
problem Underexplored how individual events influence overall dynamics over time.
method Decoupled MTPP framework using Neural Ordinary Differential Equations (Neural ODEs).
result Significantly improves performance on real-life datasets compared to state-of-the-art methods.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
An optimal life insurance policy in the investment-consumption problem in an incomplete marketq-fin.PM
This paper considers an optimal life insurance for a householder subject to mortality risk. The household receives a wage income continuously, which is terminated by unexpected (premature) loss of earning power or (planned and intended) retirement, whichever happens first. In order to hedge the risk of losing income st…
From Live to Recording: Consumer Demand and Response to Price Across the Livestreaming Lifecyclestat.ML
Study finds consumers are more price-sensitive before livestreams than after.
problem Understanding consumer demand during livestreaming lifecycle.
method Examined consumer demand for live events and recorded versions using data from a livestreaming platform.
result Demand is more price-sensitive before livestreams than after.
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.
New model captures time and mark inter-dependence in TPPs.
problem Limited predictive performance of conditionally independent TPP models on entangled time and mark interactions.
method Developed a multivariate TPP that models conditional inter-dependence of time and mark, using both intensity-based and intensity-free models.
result Proposed TPP models outperform conditionally independent and dependent models in standard prediction tasks.
Automated process discovery is a class of process mining methods that allow analysts to extract business process models from event logs. Traditional process discovery methods extract process models from a snapshot of an event log stored in its entirety. In some scenarios, however, events keep coming with a high arrival…
Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.
problem Cost-effective RUL prediction from limited, non-shared CM signals with unknown parametric forms.
method Joint modeling of nonlinear degradation signals and failure events using federated learning.
result Superior RUL prediction compared to alternatives, validated through simulations and real data.
The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, th…
SurvivalBoost improves prediction of event times in competing risks scenarios.
problem Predicting event times in scenarios with multiple possible outcomes.
method Developed a strictly proper censoring-adjusted scoring rule for stochastic optimization of competing risks.
result SurvivalBoost outperforms 12 state-of-the-art models across various metrics.
Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation …
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
problem Poor calibration of Deep Cox models' survival probabilities.
method Isotonic regression for post hoc calibration of Deep Cox models.
result Establishes favorable theoretical guarantees and demonstrates empirical effectiveness.
Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows rela…
Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)cs.AI
Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execut…
FastCPH efficiently predicts survival times using neural networks.
problem Improving efficiency and accuracy of Cox proportional hazards models in neural networks.
method Developed FastCPH, a linear-time method supporting Breslow and Efron methods for tied events.
result Outperforms existing CoxPH approaches and selects useful covariates.
A Natural Disasters Indexq-fin.RM
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
Respiratory infections and chronic respiratory diseases impose a heavy health burden worldwide. Coughing is one of the most common symptoms of many such infections, and can be indicative of flare-ups of chronic respiratory diseases. Whether at a clinical or public health level, the capacity to identify bouts of coughin…
In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I a…
Machine learning models predict depression risk based on various factors.
problem Identifying individuals at greatest risk for depression.
method Random Effects/Expectation Maximization (RE-EM) trees and Mixed Effects Random Forest (MERF) algorithms.
result Machine learning models accurately predict depression severity and identify key predictors.
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem …
In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply …
Study predicts when ALS patients will lose speech, swallowing, etc. based on covariates.
problem Predicting when ALS patients will experience significant functional decline.
method Multi-event survival analysis, covariate-based models.
result Covariate-based models outperform Kaplan-Meier estimator in predicting time-to-event outcomes.
Exclusive Lasso improves survival prediction in cancer datasets.
problem Enhanced survival prediction in cancer datasets with high-dimensional genomic and clinical data.
method Proposes Exclusive Lasso regularization for feature selection in Cox regression models for grouped variables.
result Demonstrates improved survival prediction performance using Exclusive Lasso compared to standard Cox regression.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.
problem Lack of address-level quote-lifecycle data in Polymarket prediction markets.
method Empirical analysis of 13 million order-filled events using DBSCAN clustering on a six-feature fill-side vector.
result Non-retail behavior is uni-modal, contradicting previous archetypal hypotheses.
The process of morphogenesis, which can be defined as an evolution of the form of an organism, is one of the most intriguing mysteries in the life sciences. It is clear, that gene expression patterns cannot explain the development of the precise geometry of an organism and its parts in space. Here, we suggest a set of …
We introduce an extension to Merton's famous continuous time model of optimal consumption and investment, in the spirit of previous works by Pliska and Ye, to allow for a wage earner to have a random lifetime and to use a portion of the income to purchase life insurance in order to provide for his estate, while investi…
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.
Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Predictionstat.ME
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood …
GC 2022 challenges real-time trend detection in financial tick data.
problem Efficiently detect trading trends in high-volume financial tick data.
method Real-time complex event processing of tick data, focusing on trend indicators and patterns.
result Participants must build reusable and practical solutions for real-life trading decisions.
Develops platforms to analyze social media data for human behavior and emotions.
problem Understanding human behavior and emotions from social media data.
method Self-structuring incremental machine learning, event detection, natural language processing.
result Captured salient topics and events from social media data, validated against news.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Ho…
A review of radar-based nowcasting of precipitation and applicable machine learning techniquesphysics.ao-ph
This paper reviews radar-based nowcasting techniques for short-term weather predictions.
problem Short-term weather predictions for adverse events like heavy rain and flooding.
method Analysis of existing radar-based nowcasting techniques and machine learning approaches.
result New partnerships between environmental science and machine learning can improve nowcasting.