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Found 6 papers, 0 papers with code
Augmenting Netflix Search with In-Session Adapted Recommendations
We motivate the need for recommendation systems that can cater to the members in-the-moment intent by leveraging their interactions from the current session.
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.
