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Found 7 papers, 1 papers with code
Domain-informed neural networks for interaction localization within astroparticle experiments
The resulting Domain-informed Neural Network (DiNN) limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC.
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)
We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD.
Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model
Specifically, an ensemble of logistic regression and naive Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0. 74, specificity = 0. 70, C-index = 0. 80).
Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes
Unlike most prior work on topic modeling, we apply the method to codes rather than to natural language.
Identifying Patterns of Associated-Conditions through Topic Models of Electronic Medical Records
In our study, we adapt the LDA model to identify latent topics in patients' EMRs.
Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records
Obesity is one of the leading health concerns in the United States.
Protein (Multi-)Location Prediction: Using Location Inter-Dependencies in a Probabilistic Framework
We evaluate our system on a dataset of single- and multi-localized proteins.
