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View article: Pairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation
Pairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation Open
Recommender systems (RSs) are intelligent filtering methods that suggest items to users based on their inferred preferences, derived from their interaction history on the platform. Collaborative filtering-based RSs rely on users past inter…
View article: Correction: Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness
Correction: Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness Open
View article: Artificial intelligence for precision medicine
Artificial intelligence for precision medicine Open
This review offers both an introduction to AI and a practical overview of how it is being used, and where its limitations lie, in precision medicine, with the goal of helping healthcare professionals understand these evolving tools and use…
View article: Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness
Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness Open
View article: Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis Open
Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various disciplines, anytime and anywhere. Despite this accessibil…
View article: Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR
Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR Open
View article: Factor Retention in Exploratory Multidimensional Item Response Theory
Factor Retention in Exploratory Multidimensional Item Response Theory Open
Multidimensional Item Response Theory (MIRT) is applied routinely in developing educational and psychological assessment tools, for instance, for exploring multidimensional structures of items using exploratory MIRT. A critical decision in…
View article: Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate
Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate Open
View article: Label Cluster Chains for Multi-Label Classification
Label Cluster Chains for Multi-Label Classification Open
Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), oth…
View article: CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis
CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis Open
View article: Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission Open
Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for e…
View article: Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches
Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches Open
Heart transplantation is a life-saving procedure for children affected by end-stage heart failure. However, despite recent improvements in long-term outcomes, 1-year post-transplantation mortality has remained relatively high. Accurate pre…
View article: HypeRS: Building a Hypergraph-driven ensemble Recommender System
HypeRS: Building a Hypergraph-driven ensemble Recommender System Open
Recommender systems are designed to predict user preferences over collections of items. These systems process users' previous interactions to decide which items should be ranked higher to satisfy their desires. An ensemble recommender syst…
View article: Predicting outcomes of acute kidney injury in critically ill patients using machine learning
Predicting outcomes of acute kidney injury in critically ill patients using machine learning Open
View article: Validated risk prediction models for outcomes of acute kidney injury: a systematic review
Validated risk prediction models for outcomes of acute kidney injury: a systematic review Open
Background Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortalit…
View article: Exploiting Censored Information in Self-Training for Time-to-Event Prediction
Exploiting Censored Information in Self-Training for Time-to-Event Prediction Open
A common problem in medical applications is predicting the time until an event of interest such as the onset of a disease, time to tumor recurrence, and time to mortality. Traditionally, classical survival analysis techniques have been use…
View article: BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor
BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor Open
Random forests are machine learning methods characterised by high performance and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a single decision tree. In this work we propose a …
View article: Comparison between Cystatin C- and Creatinine-Based Estimated Glomerular Filtration Rate in the Follow-Up of Patients Recovering from a Stage-3 AKI in ICU
Comparison between Cystatin C- and Creatinine-Based Estimated Glomerular Filtration Rate in the Follow-Up of Patients Recovering from a Stage-3 AKI in ICU Open
Background: Acute kidney injury (AKI) in critically ill patients is associated with a significant increase in mortality as well as long-term renal dysfunction and chronic kidney disease (CKD). Serum creatinine (SCr), the most widely used b…
View article: Online Extra Trees Regressor
Online Extra Trees Regressor Open
Data production has followed an increased growth in the last years, to the point that traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of generated data. Stream or online ML presents itself as a viabl…
View article: Predicting Survival Outcomes in the Presence of Unlabeled Data
Predicting Survival Outcomes in the Presence of Unlabeled Data Open
View article: Predicting Survival Outcomes in the Presence of Unlabeled Data
Predicting Survival Outcomes in the Presence of Unlabeled Data Open
Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial challenges, which can lead to reduced data collection and, i…
View article: Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification Open
The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies. Based on the interactions between genes (and gene products) extracted from the increasing genomic da…
View article: Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments
Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments Open
View article: BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor
BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor Open
Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined, they are not as interpre…
View article: An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering Open
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. T…
View article: Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction
Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction Open
The cold-start problem is one of the main challenges in recommender systems and specifically in collaborative filtering methods. Such methods, albeit effective, typically can not handle new items or users that do not have any prior interac…
View article: PT-MESS: A Problem-transformation Approach for Multi-event Survival Analysis
PT-MESS: A Problem-transformation Approach for Multi-event Survival Analysis Open
View article: Recommender Systems in the Real Estate Market—A Survey
Recommender Systems in the Real Estate Market—A Survey Open
The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms…
View article: Beyond global and local multi-target learning
Beyond global and local multi-target learning Open
View article: Deep tree-ensembles for multi-output prediction
Deep tree-ensembles for multi-output prediction Open