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.
TransformerLSR models longitudinal, recurrent, and survival data jointly.
Study compares resampling methods for rare event prediction in longitudinal studies.
We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of longitudinal signals and a Cox model to map time-to-event data with…
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty…
DynForest predicts event probabilities from longitudinal data, handling endogenous predictors.
TPSQRs model longitudinal event data, detecting ADRs from EHRs.
TraCeR uses transformers to analyze survival data with longitudinal covariates.
A new framework models multi-state events and biomarkers.
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes shou…
EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification, and computational difficulties when applie…
Proposes a new model for time-to-event prediction with uncertainty quantification.
Model trains passing events on a bridge using multilevel Gaussian process.
Modeling disease progression in healthcare administrative databases is complicated by the fact that patients are observed only at irregular intervals when they seek healthcare services. In a longitudinal cohort of 76,888 patients with chronic obstructive pulmonary disease (COPD), we used a continuous-time hidden Markov…
Predicts clinical events using a landmark approach with machine learning for large biomarker histories.
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to …
Develops a machine learning model to predict ALS progression and assistive device use.
A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint mod…
Develops a deep survival model for causal inference in longitudinal studies.
Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has privacy costs and resource constraints associated with it. At present, most models focus on generating cross-sectional health data which is not necessarily representative of real data. …
With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with var…
A tutorial on various methods for clustering longitudinal data.
Developed a flexible Bayesian g-formula for causal survival analysis with time-dependent confounding.
The episodic, irregular and asynchronous nature of medical data render them difficult substrates for standard machine learning algorithms. We would like to abstract away this difficulty for the class of time-stamped categorical variables (or events) by modeling them as a renewal process and inferring a probability dens…
This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, a…
LPCI provides valid prediction intervals for longitudinal data.
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data, while existing neural network mode…
Enhances patient failure prediction using dynamic survival models.
GANs improve longitudinal data imputation but face challenges in missing data and class imbalance.
New method models longitudinal data using variational inference and normalizing flows.
Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during …
Deep neural networks are a family of computational models that have led to a dramatical improvement of the state of the art in several domains such as image, voice or text analysis. These methods provide a framework to model complex, non-linear interactions in large datasets, and are naturally suited to the analysis of…
Neural network predicts cardiovascular events from EHRs with high accuracy.
latrend simplifies longitudinal clustering for numeric measurements.
HL-VAE extends VAE for heterogeneous temporal and longitudinal data.
Automates kernel discovery for longitudinal data analysis.
Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea…
Develops new algorithms for QRF to handle mixed-frequency and longitudinal data.
A scalable model for high-dimensional longitudinal data.
New method calibrates asynchronous, error-prone covariates for longitudinal data.
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
Finite mixture models have become a popular tool for clustering. Amongst other uses, they have been applied for clustering longitudinal data and clustering high-dimensional data. In the latter case, a latent Gaussian mixture model is sometimes used. Although there has been much work on clustering using latent variables…
CausalLongPFN predicts counterfactual outcomes from time-series data.
Dynamic topic model improves mental health note analysis for children.
Develops algorithm to find subgroups with different treatment effects in HIV patients.
This paper reviews random forest methods for analyzing longitudinal data in precision medicine.
We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an…