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Giovanni Cinà
Giovanni Cinà
Amsterdam University Medical Center | University of Amsterdam | Pacmed
Verified email at amsterdamumc.nl
Title
Cited by
Cited by
Year
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
D Ulmer, L Meijerink, G Cinà
Machine Learning for Health, 341-354, 2020
572020
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
LM Fleuren, TA Dam, M Tonutti, DP de Bruin, RCA Lalisang, D Gommers, ...
Critical Care 25, 1-12, 2021
342021
Know your limits: Uncertainty estimation with relu classifiers fails at reliable ood detection
D Ulmer, G Cinà
Uncertainty in Artificial Intelligence, 1766-1776, 2021
28*2021
Predictors for extubation failure in COVID-19 patients using a machine learning approach
LM Fleuren, TA Dam, M Tonutti, DP de Bruin, RCA Lalisang, D Gommers, ...
Critical Care 25, 1-10, 2021
242021
Proving classical theorems of social choice theory in modal logic
G Ciná, U Endriss
Autonomous Agents and Multi-Agent Systems 30, 963-989, 2016
192016
The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge
J de Vos, LA Visser, AA de Beer, M Fornasa, PJ Thoral, PWG Elbers, ...
Value in Health 25 (3), 359-367, 2022
172022
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
D Ruhe, G Cinà, M Tonutti, D de Bruin, P Elbers
arXiv preprint arXiv:1906.08619, 2019
152019
Why we do need Explainable AI for Healthcare
G Cinà, T Röber, R Goedhart, I Birbil
arXiv preprint arXiv:2206.15363, 2022
142022
Bisimulation for conditional modalities
A Baltag, G Ciná
Studia Logica 106, 1-33, 2018
142018
A Syntactic Proof of Arrow's Theorem in a Modal Logic of Social Choice Functions
G Ciná, U Endriss
Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015
132015
Predicting readmission or death after discharge from the icu: External validation and retraining of a machine learning model
AAH de Hond, IMJ Kant, M Fornasa, G Cinà, PWG Elbers, PJ Thoral, ...
Critical Care Medicine 51 (2), 291-300, 2023
122023
Out-of-distribution detection for medical applications: Guidelines for practical evaluation
K Zadorozhny, P Thoral, P Elbers, G Cinà
Multimodal AI in healthcare: A paradigm shift in health intelligence, 137-153, 2022
112022
Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study
SL van der Meijden, AAH de Hond, PJ Thoral, EW Steyerberg, IMJ Kant, ...
JMIR Human Factors 10, e39114, 2023
72023
Uncertainty estimation for classification and risk prediction on medical tabular data
L Meijerink, G Cinà, M Tonutti
arXiv preprint arXiv:2004.05824, 2020
7*2020
Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data …
LM Fleuren, M Tonutti, DP de Bruin, RCA Lalisang, TA Dam, D Gommers, ...
Intensive care medicine experimental 9, 1-15, 2021
62021
Impact of a Machine Learning–Based Decision Support System for Urinary Tract Infections: Prospective Observational Study in 36 Primary Care Practices
WE Herter, J Khuc, G Cinà, BJ Knottnerus, ME Numans, MA Wiewel, ...
JMIR Medical Informatics 10 (5), e27795, 2022
52022
Categories for the working modal logician
G Cinà
Ph. D. thesis, Institute for Logic, Language and Computation, University of …, 2017
42017
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients
A Izdebski, PJ Thoral, RCA Lalisang, DM McHugh, D Gommers, ...
arXiv preprint arXiv:2109.06707, 2021
22021
On the connection between the categorical and the modal logic approaches to Quantum Mechanics
G Cina
ILLC, 2013
22013
Semantic match: Debugging feature attribution methods in XAI for healthcare
G Cina, TE Rober, R Goedhard, SI Birbil
Conference on Health, Inference, and Learning, 182-190, 2023
12023
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