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FAMIGLINI, LORENZO

FAMIGLINI, LORENZO  

DIPARTIMENTO DI INFORMATICA, SISTEMISTICA E COMUNICAZIONE  

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Titolo Tipologia Data di pubblicazione Autori File
Dissimilar Similarities: Comparing Human and Statistical Similarity Evaluation in Medical AI 02 - Intervento a convegno 2024 Cabitza F.Famiglini L.Campagner A. +
Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram 01 - Articolo su rivista 2024 Famiglini, LCampagner, ACabitza, F +
Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems 01 - Articolo su rivista 2024 Famiglini, LCampagner, ACabitza, F +
Never tell me the odds: Investigating pro-hoc explanations in medical decision making 01 - Articolo su rivista 2024 Cabitza, FNatali, CFamiglini, LCampagner, A +
Biomarkers for Mixed Dementia: a hard bone to bite? Preliminary analyses and promising results for a debated topic 02 - Intervento a convegno 2023 Campagner A.Famiglini L.Rossi P.Annoni G.Cabitza F. +
Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making 02 - Intervento a convegno 2023 Natali C.Famiglini L.Campagner A.Cabitza F. +
Everything is varied: The surprising impact of instantial variation on ML reliability 01 - Articolo su rivista 2023 Campagner, AndreaFamiglini, LorenzoCabitza, Federico +
Explainability meets uncertainty quantification: Insights from feature-based model fusion on multimodal time series 01 - Articolo su rivista 2023 Famiglini L.Cabitza F. +
Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice 02 - Intervento a convegno 2023 Cabitza F.Campagner A.Famiglini L.Natali C. +
Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods, and Use 02 - Intervento a convegno 2023 Famiglini L.Campagner A.Cabitza F.
A Confidence Interval-Based Method for Classifier Re-Calibration 02 - Intervento a convegno 2022 Campagner A.Famiglini L.Cabitza F.
A parsimonious machine learning approach to detect inappropriate treatments in spine surgery on the basis of patient-reported outcomes 02 - Intervento a convegno 2022 Famiglini, LMilella, FCabitza, F +
A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients 01 - Articolo su rivista 2022 Famiglini, LCampagner, ACabitza, F +
Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine 01 - Articolo su rivista 2022 Milella, FridaFamiglini, LorenzoBanfi, GiuseppeCabitza, Federico
Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning 02 - Intervento a convegno 2022 Cabitza F.Campagner A.Famiglini L. +
Detecting the Effect Size of Weather Conditions on Patient-Reported Outcome Measures (PROMs) 01 - Articolo su rivista 2022 Milella, FridaSeveso, AndreaFamiglini, LorenzoBanfi, GiuseppeCabitza, Federico
Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models’ Calibration 02 - Intervento a convegno 2022 Cabitza F.Campagner A.Famiglini L.
How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data 01 - Articolo su rivista 2022 Milella, FridaFamiglini, LorenzoCabitza, Federico +
Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19 03 - Contributo in libro 2022 Famiglini L. +
Re-calibrating Machine Learning Models Using Confidence Interval Bounds 02 - Intervento a convegno 2022 Campagner A.Famiglini L.Cabitza F.