Develops a neural model to predict event occurrence and timing.
problem Standard event time models ignore the distinction between event occurrence probability and predicted time.
method Introduces a conditional event time model using a neural network with a binary stochastic layer.
result Shows superior event occurrence and timing predictions on various datasets.
Generates synthetic health data from patient visits.
problem Lack of longitudinal, event-based medical data.
method Transformed longitudinal data into summary statistics, trained GAN.
result Synthetic data closely resembles real data univariately.
HRTPP improves TPP interpretability and accuracy in medical event modeling.
problem Lack of interpretability in TPPs for medical event sequences.
method Hybrid-Rule Temporal Point Processes (HRTPP) integrating temporal logic rules and numerical features.
result HRTPP outperforms state-of-the-art interpretable TPPs in predictive performance and clinical interpretability.
Proposes online learning for Hawkes processes with network structure and event interaction.
problem Modeling complex interactions and latent structures in network events.
method Online learning approach for mixture of multivariate Hawkes processes.
result Efficacy demonstrated on synthetic and real-world data.
Modeling disease progression using irregular time intervals in EHRs.
problem Challenges in analyzing temporal data from EHRs.
method Developed a Markovian generative model using EHR data.
result Model accurately recovers underlying disease progression patterns from irregular time intervals.
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to …
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.
method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.
result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.
LMM predicts healthcare costs and risks with improved accuracy.
problem Wasteful healthcare spending and inefficiencies in risk prediction.
method Generative pre-trained transformer trained on patient event sequences.
result Improves cost prediction by 14.1% and chronic conditions prediction by 1.9%.
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…
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
problem Lack of historical information in single-record patients hinders modeling rare medical events.
method Hybrid framework combining supervised and unsupervised learning to integrate concurrent and single-record data.
result Single-record data and concurrent diagnoses provide valuable information for improving suicide risk modeling.
This paper uses neural networks to accurately model competing risks in survival analysis.
problem Ignoring competing risks leads to biased survival estimation in machine learning models.
method The paper introduces constrained monotonic neural networks to model each competing survival distribution.
result The method ensures exact likelihood maximization with reduced computational cost.
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, pr…
Neural network model predicts alternating event-free periods.
problem Dynamic prediction of alternating recurrent events with statistical nuance.
method Developed an online dynamic prediction framework using neural network theory.
result Outstanding performance in predicting alternating recurrent event-free time.
Study identifies risk factors for subsequent suicide attempts in youth.
problem Uncertainty in suicide attempt identification from medical claims data.
method Integrative Cox cure model with regularization for survival analysis with uncertain events.
result Identifies risk factors for subsequent suicide attempts and distinguishes susceptibility from timing.
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…
We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance…
New method identifies sepsis-related patient features in EMR data.
problem Identify sepsis-related patient features in EMR data.
method Linear multivariate Hawkes process model with ReLU link function, coupled with gradient-based method.
result Identifies several interpretable GC chains that precede sepsis.
Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that h…
TransformerLSR models longitudinal, recurrent, and survival data jointly.
problem Joint modeling of longitudinal measurements, recurrent events, and survival data with dependencies.
method Transformer-based deep learning framework integrating deep temporal point processes and latent structure representation.
result TransformerLSR effectively models all three components simultaneously, demonstrating necessity and effectiveness through simulations and real-world data.
Visual analytics system for comparing medical records using sequence embeddings.
problem Challenges in analyzing medical records due to high dimensionality, irregularity, and sparsity.
method Event and sequence embeddings using autoencoder and self-attention mechanism, with sequence alignment for comparison.
result Demonstrated effectiveness with real-world neonatal ICU dataset.
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…
Multivariate Bernoulli autoregressive (BAR) processes model time series of events in which the likelihood of current events is determined by the times and locations of past events. These processes can be used to model nonlinear dynamical systems corresponding to criminal activity, responses of patients to different med…
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
Develops a method to simulate rare dangerous events in autonomous systems.
problem Rare dangerous events in safety-critical systems are hard to test in real-world settings.
method Combines exploration, exploitation, and optimization techniques for rare-event simulation.
result Provides rigorous guarantees for the performance of the method.
Improved neural models for diverse user event sequences.
problem Challenges in modeling diverse user event sequences.
method Mixtures of latent embeddings with amortized variational inference.
result Systematic improvements over existing work for various predictive metrics.
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
problem Limited EMR embedding methods fail to capture patient demographics, utilisation, and code descriptions.
method MedGraph constructs an attributed bipartite graph and uses a point process to model temporal sequences.
result MedGraph outperforms state-of-the-art methods in medical risk prediction tasks.
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…
25% of people who received a liver transplant will go on to develop diabetes within the next 5 years. These thousands of individuals are at 2-fold higher risk of cardiovascular events, graft loss, infections, as well as lower long-term survival. This is partly due to the medication used during and/or after transplant t…
Study predicts colorectal polyp recurrence using medical records and statistical models.
problem Identifying patient characteristics influencing colorectal polyp recurrence.
method Natural language processing for extracting polyp characteristics, Kaplan-Meier curves, Cox proportional hazards modeling, random survival forest models.
result Polyp size, number, location, and patient smoking status significantly influence recurrence risk.
Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, pre…
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connecti…
Comprisk simplifies competing-risks analysis in Python.
problem Analyzing medical time-to-event data with competing risks.
method A scikit-learn-compatible toolkit for competing-risks survival analysis.
result Comprisk provides a unified API for various competing-risks methods.
Data of practical interest - such as personal records, transaction logs, and medical histories - are sequential collections of events relevant to a particular source entity. Recent studies have attempted to link sequences that represent a common entity across data sets to allow more comprehensive statistical analyses a…
This monograph introduces deep learning models for predicting time-to-event outcomes.
problem Predicting critical events and their timing from time series data.
method Neural networks and deep learning models for survival analysis.
result Improved accuracy in predicting time-to-event outcomes using deep learning.
New framework models time-uncertain point processes for better event prediction.
problem Uncertainty in event times in point processes.
method Formulated and discretized continuous-time Hawkes processes with time grid, enabling optimization methods for inference.
result Parameter recovery with O(1/k) convergence rate using gradient descent and VI. Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not have the equation of motion describing the flows, or how they are affected by jumps. To this end, we introduce Neural Jump Stochastic Different…
Latent Block-Diffusion Temporal Point Processes (LBDTPP) is a semi-autoregressive framework for generating asynchronous event sequences.
problem Generating asynchronous event sequences
method Latent Block-Diffusion Temporal Point Processes
result Outperforms state-of-the-art TPP baselines in both unconditional and conditional generation tasks
Machine learning detects and diagnoses coughs for respiratory infections.
problem Detecting and diagnosing coughs for respiratory infections.
method Convolutional Neural Networks (CNNs) to detect cough events and diagnose three illnesses.
result Accuracy over 89% for detection and diagnosis.
We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is…
SAVAE uses deep learning for survival analysis, improving model performance and interpretability.
problem Complex medical data with censoring and covariate interactions.
method SAVAE is a Variational Autoencoder tailored for survival analysis, using a novel ELBO formulation.
result SAVAE outperforms state-of-the-art techniques in various datasets, demonstrating robustness and interpretability.
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.
Gradient-based methods improve understanding of deep learning survival models.
problem Limited interpretability of deep learning survival models hinders their adoption.
method Gradient-based explanation methods tailored to survival neural networks.
result Gradient-based methods capture feature effects and temporal dynamics.
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…
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.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
problem Continuous treatment recommendation in medical settings with survival data.
method Deep Survival Dose Response Function (DeepSDRF) for learning conditional average dose response (CADR) function.
result Similar performance of recommender algorithms based on random search and reinforcement learning.
New method for discrete-time survival analysis with competing risks.
problem Discrete failure time data in survival analysis.
method Proposes a new estimation procedure for discrete-time survival analysis with competing events.
result Offers advantages over existing procedures and integrates regularized regression methods.
New monitoring method detects ML risk models' performance changes in medical interventions.
problem Monitoring ML risk models in healthcare is complicated by confounding medical interventions.
method Developed a new score-based CUSUM monitoring procedure with dynamic control limits.
result Valid inference is possible if conditional exchangeability or time-constant selection bias hold.
A new method models individual survival curves using conditional normalizing flows.
problem Precise per-individual predictions in survival analysis.
method Conditional normalizing flows for flexible and individualized survival distributions.
result Efficient estimation of individual survival curves without overfitting.