218 citations · 369 across the 7 of their papers we have counts for
7 papers
World of ScoreCraft: Novel Multi Scorer Experiment on the Impact of a Decision Support System in Sleep Staging
Benedikt Holm, Arnar Óskarsson, Björn Elvar Þorleifsson +8
Manual scoring of polysomnography (PSG) is a time intensive task, prone to inter scorer variability that can impact diagnostic reliability. This study investigates the integration…
An Optimized Framework for Processing Large-scale Polysomnographic Data Incorporating Expert Human Oversight
Benedikt Holm, Gabriel Jouan, Emil Hardarson +6
Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning metho…
Changes in mobility patterns in Europe during the COVID-19 pandemic: Novel insights using open source data
Anna Sigridur Islind, María Óskarsdóttir, Harpa Steingrímsdóttir
The COVID-19 pandemic has changed the way we act, interact and move around in the world. The pandemic triggered a worldwide health crisis that has been tackled using a variety of s…
The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics
María Óskarsdóttir, Cristián Bravo, Carlos Sarraute +2
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improv…
Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending
María Óskarsdóttir, Cristián Bravo, Carlos Sarraute +2
Globally, two billion people and more than half of the poorest adults do not use formal financial services. Consequently, there is increased emphasis on developing financial techno…
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture
María Óskarsdóttir, Cristián Bravo, Wouter Verbeke +3
Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational lear…
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
Maria Óskarsdóttir, Cristián Bravo, Wouter Verbeke +3
Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference method…