Enrico Longato

Orcid: 0000-0001-5940-645X

According to our database1, Enrico Longato authored at least 26 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study.
BMC Medical Informatics Decis. Mak., December, 2024

Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes.
CoRR, 2024

Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression.
CoRR, 2024

Unveiling Trustworthy AI Challenges: Characterizing Prediction Reliability.
Proceedings of the 8th IEEE Forum on Research and Technologies for Society and Industry Innovation, 2024

Using Wearable and Environmental Data to Improve the Prediction of Amyotrophic Lateral Sclerosis and Multiple Sclerosis Progression: an Explorative Study.
Proceedings of the Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), 2024



2023

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review.
Artif. Intell. Medicine, August, 2023

DAVI: A Dataset for Automatic Variant Interpretation.
Proceedings of the Experimental IR Meets Multilinguality, Multimodality, and Interaction, 2023

Baseline Machine Learning Approaches To Predict Multiple Sclerosis Disease Progression.
Proceedings of the Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2023), 2023



Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral Sclerosis.
Proceedings of the Artificial Intelligence in Medicine, 2023

2022
Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes.
Comput. Methods Programs Biomed., 2022

Explainable Deep-Learning Model Reveals Past Cardiovascular Disease in Patients with Diabetes Using Free-Form Visit Reports.
Proceedings of the Machine Learning, Optimization, and Data Science, 2022

Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease Progression.
Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, September 5th - to, 2022


Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge.
Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, September 5th - to, 2022

2021
A Deep Learning Approach to Predict Diabetes' Cardiovascular Complications From Administrative Claims.
IEEE J. Biomed. Health Informatics, 2021

Comparing the Predictive Power of Heart Failure Hospitalisation Risk Scores in the Diabetic Outpatient Clinic and Primary Care Settings.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

Recurrent Neural Network to Predict Renal Function Impairment in Diabetic Patients via Longitudinal Routine Check-up Data.
Proceedings of the Artificial Intelligence in Medicine, 2021

2020
A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models.
J. Biomed. Informatics, 2020

2019
Detecting Undiagnosed Diabetes: Proof-of-Concept Based on the Health-Information Exchange System of the Veneto Region (North-East Italy).
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

2018
Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data.
Comput. Biol. Medicine, 2018

Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018


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