Jerónimo Hernández-González
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View article: Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning
Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning Open
Mental illnesses affect almost 15% of the world’s population, with half of the cases emerging before age 14. Improved methods for predicting mental distress among adolescents, particularly in vulnerable populations, are needed. This study …
View article: An Information-Bottleneck Based Approach for Learning Using Privileged Information
An Information-Bottleneck Based Approach for Learning Using Privileged Information Open
Learning using privileged information assumes that a subset of the descriptive features, aka privileged variables, available for learning a predictive model is no longer available in prediction time. The objective is to adjust a machine le…
View article: 2D/3D Pattern Formation Comparison Using Spectral Methods to Solve Nonlinear Partial Differential Equations of Condensed and Soft Matter
2D/3D Pattern Formation Comparison Using Spectral Methods to Solve Nonlinear Partial Differential Equations of Condensed and Soft Matter Open
It is well known that nonlinear partial differential equations (NLPDEs) can only be solved numerically and that fourth-order NLPDEs in their derivatives require unconventional methods. This paper explains spectral numerical methods for obt…
View article: Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning
Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning Open
Mental illnesses affect almost 15% of the world's population, with half of the cases emerging before age 14. Improved methods for predicting mental distress among adolescents, particularly in vulnerable populations, are needed. This study …
View article: Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction
Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction Open
Recent evidence suggests that psycho-cardio-metabolic (PCM) multimorbidity finds its origins in exposure to early-life factors (ELFs), making the exploration of this association crucial for understanding and effective management of these c…
View article: Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning
Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning Open
Mental illnesses affect almost 15% of the world's population, with half of the cases emerging before age 14. Improved methods for predicting the progression of mental distress among adolescents, particularly in vulnerable populations, are …
View article: Modeling river flow for flood forecasting: A case study on the Ter river
Modeling river flow for flood forecasting: A case study on the Ter river Open
Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when…
View article: Fairness and bias correction in machine learning for depression prediction across four study populations
Fairness and bias correction in machine learning for depression prediction across four study populations Open
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (…
View article: Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study*
Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study* Open
OBJECTIVES: To assess the value of machine learning approaches in the development of a multivariable model for early prediction of ICU death in patients with acute respiratory distress syndrome (ARDS). DESIGN: A development, testing, and e…
View article: Machine and deep learning for longitudinal biomedical data: a review of methods and applications
Machine and deep learning for longitudinal biomedical data: a review of methods and applications Open
Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthca…
View article: Fairness and bias correction in machine learning for depression prediction: results from four study populations
Fairness and bias correction in machine learning for depression prediction: results from four study populations Open
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (…
View article: On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels
On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels Open
The use of crowdsourcing for annotating data has become a popular and cheap alternative to expert labelling. As a consequence, an aggregation task is required to combine the different labels provided and agree on a single one per example. …
View article: WhichDog: A crowdsourced dataset including candidate set-based labelling
WhichDog: A crowdsourced dataset including candidate set-based labelling Open
A dataset with crowdsourced labels for aggregation and supervised classification. It contains 400 images of dogs from the Stanford Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). Images of dogs that belong to 32 different…
View article: WhichDog: A crowdsourced dataset including candidate set-based labelling
WhichDog: A crowdsourced dataset including candidate set-based labelling Open
A dataset with crowdsourced labels for aggregation and supervised classification. It contains 400 images of dogs from the Stanford Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). Images of dogs that belong to 32 different…
View article: Machine Learning From Crowds Using Candidate Set-Based Labeling
Machine Learning From Crowds Using Candidate Set-Based Labeling Open
Crowdsourcing is a popular cheap alternative in machine learning for gathering information from a set of annotators. Learning from crowd-labelled data involves dealing with its inherent uncertainty and inconsistencies. In the classical fra…
View article: Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection
Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection Open
Embryo selection is a critical step in assisted reproduction (ART): a good selection criteria is expected to increase the probability of inducing pregnancy. In the past, machine learning methods have been used to predict implantation and t…
View article: A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data
A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data Open
Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led …
View article: A Robust Solution to Variational Importance Sampling of Minimum Variance
A Robust Solution to Variational Importance Sampling of Minimum Variance Open
Importance sampling is a Monte Carlo method where samples are obtained from an alternative proposal distribution. This can be used to focus the sampling process in the relevant parts of space, thus reducing the variance. Selecting the prop…
View article: Variational Importance Sampling: Initial Findings
Variational Importance Sampling: Initial Findings Open
Importance sampling is a Monte Carlo method that samples from an alternative distribution, the proposal distribution. It focuses the sampling process in the interesting parts of space reducing the variance. The efficiency of importance sam…
View article: Candidate Labeling for Crowd Learning
Candidate Labeling for Crowd Learning Open
Crowdsourcing has become very popular among the machine learning community as a way to obtain labels that allow a ground truth to be estimated for a given dataset. In most of the approaches that use crowdsourced labels, annotators are aske…
View article: Weak Labeling for Crowd Learning.
Weak Labeling for Crowd Learning. Open
Crowdsourcing has become very popular among the machine learning community as a way to obtain labels that allow a ground truth to be estimated for a given dataset. In most of the approaches that use crowdsourced labels, annotators are aske…
View article: Two datasets of defect reports labeled by a crowd of annotators of unknown reliability
Two datasets of defect reports labeled by a crowd of annotators of unknown reliability Open
Classifying software defects according to any defined taxonomy is not straightforward. In order to be used for automatizing the classification of software defects, two sets of defect reports were collected from public issue tracking system…
View article: Output Feedback Self-tuning Wavenet Control for Underactuated Euler-Lagrange Systems
Output Feedback Self-tuning Wavenet Control for Underactuated Euler-Lagrange Systems Open
There exist some physical systems whose underactuation and limited sensing represent a challenge in engineering. The former requires a model-based nesting or virtual control schemes to control the underactuated degrees of freedom through t…
View article: Merging knowledge bases in different languages
Merging knowledge bases in different languages Open
Recently, different systems which learn to populate and extend a knowledge base (KB) from the web in different languages have been presented. Although a large set of concepts should be learnt independently from the language used to read, t…
View article: Fitting the data from embryo implantation prediction: Learning from label proportions
Fitting the data from embryo implantation prediction: Learning from label proportions Open
Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve machine learning tech…