Disease Trajectory Forecasting
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Benchmarks
UK CF trust
Most implemented
CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data
Papers
Generating synthetic multi-dimensional molecular-mediator time series data for artificial intelligence-based disease trajectory forecasting and drug development digital twins: Considerations
The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other doma…
Disease Trajectory ForecastingTime SeriesTrajectory ForecastingClinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data
Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory …
Decision MakingDisease Trajectory ForecastingMedical DiagnosisPrognosis+1CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting
In medical applications, deep learning methods are built to automate diagnostic tasks. However, a clinically relevant question that practitioners usually face, is how to predict the future trajectory of a disease (progno…
Decision MakingDiagnosticDisease Trajectory ForecastingPrognosis+1Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relatio…
Disease Trajectory ForecastingSurvival AnalysisForecasting Individualized Disease Trajectories using Interpretable Deep Learning
Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either accurate predictions of patients prognose…
Deep LearningDisease PredictionDisease Trajectory ForecastingState Space ModelsRETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus l…
Disease Trajectory Forecasting