Search Results for author:
Found 7 papers, 2 papers with code
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering
This method recommends relevant information from electronic health records for physicians during patient visits.
Hybrid Collaborative Filtering Models for Clinical Search Recommendation
With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in clinics.
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket.
HAM: Hybrid Associations Models for Sequential Recommendation
We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.
ALE: Additive Latent Effect Models for Grade Prediction
Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e. g., next term).
Grade Prediction with Temporal Course-wise Influence
The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space.
Predicting Performance on MOOC Assessments using Multi-Regression Models
The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs).
