Machine learning approach to identify early predictors of MS progression: the NeuroArtP3 project

被引：0

作者：

Poretto, Valentina ^{[1
]}

Lapucci, Caterina ^{[2
]}

Betti, Matteo ^{[3
]}

Bellinvia, Angelo ^{[3
]}

Endrizzi, Walter ^{[4
]}

Ragni, Flavio ^{[4
]}

Bovo, Stefano ^{[4
]}

Longo, Chiara ^{[1
]}

Carpi, Elisabetta ^{[2
]}

Moroni, Monica ^{[4
]}

Chierici, Marco ^{[4
]}

Jurman, Giuseppe ^{[4
]}

Osmani, Venet ^{[2
]}

Piana, Michele ^{[5
]}

Marenco, Manuela ^{[2
]}

Marangoni, Sabrina ^{[1
]}

Portaccio, Emilio ^{[3
]}

Giometto, Bruno ^{[1
,6
]}

Inglese, Matilde ^{[2
,7
]}

Antonio, Ucccelli ^{[2
]}

Amato, Maria Pia ^{[3
,8
]}

机构：

[1] Azienda Prov Serv Sanit APSS, Neurol Unit, Trento, Italy

[2] IRCCS Osped Policlin San Martino, Genoa, Italy

[3] Univ Florence, Dept NEUROFARBA, Florence, Italy

[4] Fdn Bruno Kessler, Data Sci Hlth, Trento, Italy

[5] Univ Genoa, IRCCS Osped Policlin San Martino, Dipartimento Matemat, Genoa, Italy

[6] Univ Trento, Ctr Interdipartimentale Sci Med CISMed, Fac Med & Chirurg, Trento, Italy

[7] Univ Genoa, Dept Neurol Rehabil Ophthalmol Genet Maternal & C, Genoa, Italy

[8] IRCCS Don Carlo Gnocchi Fdn, Florence, Italy

来源：

MULTIPLE SCLEROSIS JOURNAL | 2024年 / 30卷 / 03期

关键词：

D O I：

暂无

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

P1577/791

引用

页码：997 / 997

页数：1

共 50 条

[21] A machine learning approach for modeling algorithm performance predictors
Pérez, J
Pazos, RA
Frausto, J
Cruz, L
Fraire, H
Santiago, E
García, NE
MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2004, 3131 : 70 - 80
[22] Using machine-learning methods to identify early-life predictors of 11-year language outcome
Gasparini, Loretta
Shepherd, Daisy A.
Bavin, Edith L.
Eadie, Patricia
Reilly, Sheena
Morgan, Angela T.
Wake, Melissa
JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY, 2022, : 1242 - 1252
[23] Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression
Cazzolli, Carlotta
Chierici, Marco
Dallabona, Monica
Guella, Chiara
Jurman, Giuseppe
AGING CLINICAL AND EXPERIMENTAL RESEARCH, 2025, 37 (01)
[24] Selecting structural MRI predictors of cognitive status in MS patients through a machine learning approach applied to a large multicenter MS population
d'Ambrosio, A.
Marzi, C.
Bisecco, A.
Diciotti, S.
Altieri, M.
Battaglini, M.
De Stefano, N.
Pagani, E.
Pantano, P.
Piervincenzi, C.
Rocca, M. A.
Storelli, L.
Filippi, M.
Tedeschi, G.
Gallo, A.
MULTIPLE SCLEROSIS JOURNAL, 2021, 27 (2_SUPPL) : 85 - 86
[25] Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store
Parsons, Thomas D.
Mcmahan, Timothy
Asbee, Justin
VIRTUAL REALITY, 2024, 28 (01)
[26] Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store
Thomas D. Parsons
Timothy McMahan
Justin Asbee
Virtual Reality, 2024, 28
[27] Machine learning models identify gene predictors of waggle dance behaviour in honeybees
Veiner, Marcell
Morimoto, Juliano
Leadbeater, Ellouise
Manfredini, Fabio
MOLECULAR ECOLOGY RESOURCES, 2022, 22 (06) : 2248 - 2261
[28] Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians
Amador, Luis
Arroyo-Torres, Irvin
Barrow, Lisa N.
JOURNAL OF BIOGEOGRAPHY, 2024, 51 (05) : 909 - 923
[29] Machine learning approach to identify users across their digital devices
Renov, Oleksii
Raj, Thakur
2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), 2015, : 1676 - 1680
[30] A Machine Learning based Approach to Identify SQL Injection Vulnerabilities
Zhang, Kevin
34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2019), 2019, : 1286 - 1288

← 1 2 3 4 5 →