Jerónimo Hernández-González

Lecturer

Universitat de Girona

Spain

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About

Jerónimo Hernández-González is a Lecturer at Universitat de Girona, Spain. His research areas encompass machine learning applications in healthcare, particularly focusing on depression prediction and ICU mortality in acute respiratory distress syndrome patients. He also investigates modeling in environmental science, specifically river flow for flood forecasting, and the use of probabilistic graphical models in assisted reproductive technologies.

Articles (14)

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2021

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2020

Collaborators (4)

Jesus Cerquides

SPAIN

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Maria Salamó Llorente

Tenured Lecturer

University of Barcelona

SPAIN

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Pedro Javier Herrera

National University of Distance Education

SPAIN

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Jesus Rico-Feijoo

Postsurgical Critical care. Unit head

SPAIN

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Jerónimo Hernández-González

Research Interests

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About

Articles (14)

Modeling river flow for flood forecasting: A case study on the Ter river

Fairness and bias correction in machine learning for depression prediction across four study populations

Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study.

Machine and deep learning for longitudinal biomedical data: a review of methods and applications

On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique

Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study

Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels

Machine learning from crowds using candidate set-based labelling

Machine Learning From Crowds Using Candidate Set-Based Labeling

On the relative value of weak information of supervision for learning generative models: An empirical study

On the relative value of weak information of supervision for learning generative models: An empirical study

A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

A Robust Solution to Variational Importance Sampling of Minimum Variance

Collaborators (4)

Jesus Cerquides

Maria Salamó Llorente

Pedro Javier Herrera

Jesus Rico-Feijoo

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