… In this paper, we describe the unsupervised learning … phenotype representations using a
mini-cluster with 14 Jetson TX2 in order to distribute training and to obtain a patient phenotype …

… to automated phenotype extractions [3]. In this paper, we describe the unsupervised learning
method for mining her data and build low-dimensional phenotype representations using a …

… In this paper, we describe the unsupervised learning method for … phenotype representations
using a mini-cluster with 14 Jetson TX2 in order to distribute training and to obtain a patient …

… In this paper, we describe the unsupervised learning method for … phenotype representations
using a mini-cluster with 14 Jetson TX2 in order to distribute training and to obtain a patient …

… accurate phenotyping 48 remains a core challenge. We proposed a general unsupervised
… Cases were defined as having at least one in-patient diagnosis or two out-patient diagnoses…

… unsupervised framework based on deep learning to process heterogeneous EHRs and derive
patient representations … diagnoses in the EHRs (ie, disease phenotyping 18 ). To this end, …

… Our goal, however, is to provide unsupervised methods that are more robust and generalizable
to large-scale data. Given the increasing availability of electronic health records and …

… ’s EHR records to visualize the patients, we find that the representations exhibit a complex …
be performed in parallel. Since the sentence matrix is stored efficiently, each process does not …

… parallel (which will increase training time). Also, RNNs only … clinical events (especially
phenotypes, comorbidities, and … In all forms of unsupervised learning, patient representations …

… variables and length T, we can represent it as a matrix X ∈ RP … (or diagnose) a patient after
each new observation while also … We use these subsequences to train a single unsupervised …