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Found 7 papers, 2 papers with code
From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE
Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient.
Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction
Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions.
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE
We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data.
Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhood variation in vulnerability to climate change-related health hazards
In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard.
Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival Data
In the pan-cancer dataset, we showed agreement in most variables but also an opposite finding in two of the explanatory variables between the CoxPH and XGBoost result.
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling
The longitudinal analysis of patient response time course following doses of therapeutics is currently performed using Pharmacokinetic/Pharmacodynamic (PK/PD) methodologies, which requires significant human experience and expertise in the modeling of dynamical systems.
Deep Poisson Factor Modeling
We propose a new deep architecture for topic modeling, based on Poisson Factor Analysis (PFA) modules.
