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Found 7 papers, 1 papers with code
Sequential Inference of Hospitalization ElectronicHealth Records Using Probabilistic Models
Inference algorithms are derived that use partial data to infer properties of the complete sequences, including their length and presence of specific values.
Unsupervised Probabilistic Models for Sequential Electronic Health Records
We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data.
Attend and Decode: 4D fMRI Task State Decoding Using Attention Models
We also investigate the transferability of BAnD's extracted features on unseen HCP tasks, either by freezing the spatial feature extraction layers and retraining the temporal model, or finetuning the entire model.
Regularized Sparse Gaussian Processes
An issue faced by SGP, especially in latent variable models, is the inefficient learning of the inducing inputs, which leads to poor model prediction.
Nonstationary Multivariate Gaussian Processes for Electronic Health Records
We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the correlations between the observed output, are all time dependent.
Modeling sepsis progression using hidden Markov models
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment.
Robust Decentralized Learning Using ADMM with Unreliable Agents
We also provide conditions on the erroneous updates for exact convergence to the optimal solution.
