Enrico Longato

发表

A Deep Learning Approach to Predict Diabetes’ Cardiovascular Complications From Administrative Claims

B. Di Camillo, Lara Tramontan, A. Avogaro, 2021, IEEE Journal of Biomedical and Health Informatics.

Improved long-term cardiovascular outcomes after intensive versus standard screening of diabetic complications: an observational study

B. Di Camillo, A. Avogaro, G. Fadini, 2019, Cardiovascular Diabetology.

Management of type 2 diabetes with a treat-to-benefit approach improved long-term cardiovascular outcomes under routine care

B. Di Camillo, A. Avogaro, G. Fadini, 2022, Cardiovascular diabetology.

Development and Validation of a Multivariable Risk Prediction Model for Sudden Cardiac Death after Myocardial Infarction (PROFID Risk Model): Study Rationale, Design and Protocol

Katherine C. Wu, G. Hindricks, G. Pontone, 2021, medRxiv.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

Sara C. Madeira, R. Bellazzi, B. Di Camillo, 2023, Artif. Intell. Medicine.

A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models

Enrico Longato, Martina Vettoretti, Barbara Di Camillo, 2020, J. Biomed. Informatics.

Exposure to dipeptidyl‐peptidase‐4 inhibitors and COVID‐19 among people with type 2 diabetes: A case‐control study

B. Di Camillo, Lara Tramontan, A. Avogaro, 2020, Diabetes, obesity & metabolism.

Prevalence and impact of diabetes among people infected with SARS-CoV-2

A. Avogaro, G. Fadini, M. Morieri, 2020, Journal of Endocrinological Investigation.

Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

Giovanni Sparacino, Andrea Facchinetti, Giada Acciaroli, 2018, Comput. Biol. Medicine.

Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions

B. Di Camillo, D. Siscovick, M. Carnethon, 2020, BMJ open diabetes research & care.

Diabetes diagnosis from administrative claims and estimation of the true prevalence of diabetes among 4.2 million individuals of the Veneto region (North East Italy).

Giovanni Sparacino, Enrico Longato, Barbara Di Camillo, 2019, Nutrition, metabolism, and cardiovascular diseases : NMCD.

Detecting Undiagnosed Diabetes: Proof-of-Concept Based on the Health-Information Exchange System of the Veneto Region (North-East Italy)

Giovanni Sparacino, Enrico Longato, Barbara Di Camillo, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Time-resolved trajectory of glucose lowering medications and cardiovascular outcomes in type 2 diabetes: a recurrent neural network analysis

B. Di Camillo, A. Avogaro, G. Fadini, 2022, Cardiovascular Diabetology.

Cardiovascular effectiveness of human-based vs. exendin-based glucagon like peptide-1 receptor agonists: a retrospective study in patients with type 2 diabetes.

B. Di Camillo, Lara Tramontan, A. Avogaro, 2020, European journal of preventive cardiology.

Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease Progression

B. Camillo, M. Vettoretti, E. Longato, 2022, CLEF.

Cardiovascular outcomes of type 2 diabetic patients treated with SGLT-2 inhibitors versus GLP-1 receptor agonists in real-life

B. Di Camillo, A. Avogaro, G. Fadini, 2020, BMJ open diabetes research & care.

Recurrent Neural Network to Predict Renal Function Impairment in Diabetic Patients via Longitudinal Routine Check-up Data

Giovanni Sparacino, Enrico Longato, Barbara Di Camillo, 2021, AIME.

Baseline Machine Learning Approaches To Predict Multiple Sclerosis Disease Progression

B. Camillo, Alessandro Guazzo, E. Tavazzi, 2023, CLEF.

Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes

A. Avogaro, B. Camillo, G. Fadini, 2022, Computer Methods and Programs in Biomedicine.

Better cardiovascular outcomes of type 2 diabetic patients treated with GLP-1 receptor agonists versus DPP-4 inhibitors in clinical practice

B. Di Camillo, Lara Tramontan, A. Avogaro, 2020, Cardiovascular Diabetology.

Exposure to dipeptidyl‐peptidase 4 inhibitors and the risk of pneumonia among people with type 2 diabetes: Retrospective cohort study and meta‐analysis

Lara Tramontan, A. Avogaro, G. Fadini, 2020, Diabetes, obesity & metabolism.

Cardiovascular outcomes after initiating GLP-1 receptor agonist or basal insulin for the routine treatment of type 2 diabetes: a region-wide retrospective study

B. Di Camillo, Lara Tramontan, A. Avogaro, 2021, Cardiovascular Diabetology.

Comparative renal outcomes of matched cohorts of patients with type 2 diabetes receiving SGLT2 inhibitors or GLP-1 receptor agonists under routine care.

B. Fattor, M. Morieri, M. Rigato, 2024, Diabetologia.

Long-term benefits of dapagliflozin on renal outcomes of type 2 diabetes under routine care: a comparative effectiveness study on propensity score matched cohorts at low renal risk

G. Beccuti, S. Piro, E. Orsi, 2024, The Lancet regional health. Europe.

Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes

B. Camillo, G. Fadini, M. Morieri, 2022, Computer Methods and Programs in Biomedicine.

Prediction of Cardiovascular Complications in Diabetes from Pharmacy Administrative Claims

B. Di Camillo, G. Fadini, G. Sparacino, 2020, 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON).