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Damiano Verda

Explore the profile of Damiano Verda including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 10
Citations 34
Followers 0
Related Specialties
Medical Informatics
General Medicine
Biology
Top 10 Co-Authors
Marco Muselli
Rita Zilich
Pietro Invernizzi
Paola Ponzani
Laura Cristoferi
Nicoletta Musacchio
Alessio Gerussi
Marco Carbone
Giacomo Guaita
Pierluigi Santin
Published In
Health Informatics J
Epidemiol Health
Front Immunol
Int J Med Inform
BMC Bioinformatics
Affiliations
Soon will be listed here.
Recent Articles
1.
Lipid-lowering therapy and LDL target attainment in type 2 diabetes: trends from the Italian Associations of Medical Diabetologists database
Rossi A, Masi D, Zilich R, Baccetti F, Baronti W, Falcetta P, et al.
Cardiovasc Diabetol . 2025 Feb; 24(1):94. PMID: 40022078
Background: Hypercholesterolemia is a major cardiovascular risk factor, particularly in individuals with type 2 diabetes (T2DM), where cardiovascular events are more prevalent. Adherence to low-density lipoprotein cholesterol (LDL-c) targets remains...
2.
Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis
Gerussi A, Saldanha O, Cazzaniga G, Verda D, Carrero Z, Engel B, et al.
JHEP Rep . 2025 Jan; 7(2):101198. PMID: 39829723
Background & Aims: Biliary abnormalities in autoimmune hepatitis (AIH) and interface hepatitis in primary biliary cholangitis (PBC) occur frequently, and misinterpretation may lead to therapeutic mistakes with a negative impact...
3.
A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy
Musacchio N, Zilich R, Masi D, Baccetti F, Nreu B, Bruno Giorda C, et al.
Int J Med Inform . 2024 Jul; 190:105550. PMID: 39059083
Aims: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy. Methods:...
4.
Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in Type 2 Diabetes: A Machine-learning Projection Using Data from Clinical Practice
Bruno Giorda C, Rossi A, Baccetti F, Zilich R, Romeo F, Besmir N, et al.
Clin Ther . 2023 Jul; 45(8):754-761. PMID: 37451913
Purpose: Recently, the 2022 American Diabetes Association and European Association for the Study of Diabetes (ADA-EASD) consensus report stressed the importance of weight control in the management of patients with...
5.
Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group
Masi D, Zilich R, Candido R, Giancaterini A, Guaita G, Muselli M, et al.
J Clin Med . 2023 Jun; 12(12). PMID: 37373787
Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal...
6.
Artificial intelligence for precision medicine in autoimmune liver disease
Gerussi A, Scaravaglio M, Cristoferi L, Verda D, Milani C, De Bernardi E, et al.
Front Immunol . 2022 Nov; 13:966329. PMID: 36439097
Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree...
7.
Validation of a predictive model for obstructive sleep apnea in people with Down syndrome
Skotko B, Garza Flores A, Elsharkawi I, Patsiogiannis V, McDonough M, Verda D, et al.
Am J Med Genet A . 2022 Nov; 191(2):518-525. PMID: 36426646
Detecting obstructive sleep apnea (OSA) is important to both prevent significant comorbidities in people with Down syndrome (DS) and untangle contributions to other behavioral and mental health diagnoses. However, laboratory-based...
8.
LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis
Gerussi A, Verda D, Cappadona C, Cristoferi L, Bernasconi D, Bottaro S, et al.
J Pers Med . 2022 Oct; 12(10). PMID: 36294727
Background: The application of Machine Learning (ML) to genetic individual-level data represents a foreseeable advancement for the field, which is still in its infancy. Here, we aimed to evaluate the...
9.
The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers
Parodi S, Verda D, Bagnasco F, Muselli M
Epidemiol Health . 2022 Oct; 44:e2022088. PMID: 36265519
Objectives: The area under a receiver operating characteristic (ROC) curve (AUC) is a popular measure of pure diagnostic accuracy that is independent from the proportion of diseased subjects in the...
10.
Machine learning in primary biliary cholangitis: A novel approach for risk stratification
Gerussi A, Verda D, Bernasconi D, Carbone M, Komori A, Abe M, et al.
Liver Int . 2021 Dec; 42(3):615-627. PMID: 34951722
Background & Aims: Machine learning (ML) provides new approaches for prognostication through the identification of novel subgroups of patients. We explored whether ML could support disease sub-phenotyping and risk stratification...