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
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
收藏
页码:997 / 997
页数:1
相关论文
共 50 条
  • [41] Interdisciplinary Project Based Learning Approach for Machine Learning and Internet of Things
    Khan, Muhammad
    Ibrahim, Mohamed
    Wu, Nansong
    Patil, Rajvardhan
    2020 9TH IEEE INTEGRATED STEM EDUCATION CONFERENCE (ISEC 2020), 2020,
  • [42] A MACHINE LEARNING APPROACH FOR EARLY DIAGNOSIS OF COGNITIVE IMPAIRMENT USING NEUROIMAGING MARKERS AND CLINICAL RISK PREDICTORS
    Tan, W. Y.
    Hilal, S.
    INTERNATIONAL JOURNAL OF STROKE, 2022, 17 (3_SUPPL) : 64 - 64
  • [43] A Machine Learning Approach for the Early Detection of Dementia
    Broman, Sven
    O'Hara, Elizabeth
    Ali, Md Liakat
    2022 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS), 2022, : 825 - 830
  • [44] Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
    Van Lissa, Caspar J.
    Stroebe, Wolfgang
    VanDellen, Michelle R.
    Leander, N. Pontus
    Agostini, Maximilian
    Draws, Tim
    Grygoryshyn, Andrii
    Gutzgow, Ben
    Kreienkamp, Jannis
    Vetter, Clara S.
    Abakoumkin, Georgios
    Khaiyom, Jamilah Hanum Abdul
    Ahmedi, Vjolica
    Akkas, Handan
    Almenara, Carlos A.
    Atta, Mohsin
    Bagci, Sabahat Cigdem
    Basel, Sima
    Kida, Edona Berisha
    Bernardo, Allan B., I
    Buttrick, Nicholas R.
    Chobthamkit, Phatthanakit
    Choi, Hoon-Seok
    Cristea, Mioara
    Csaba, Sara
    Damnjanovic, Kaja
    Danyliuk, Ivan
    Dash, Arobindu
    Di Santo, Daniela
    Douglas, Karen M.
    Enea, Violeta
    Faller, Daiane Gracieli
    Fitzsimons, Gavan J.
    Gheorghiu, Alexandra
    Gomez, Angel
    Hamaidia, Ali
    Han, Qing
    Helmy, Mai
    Hudiyana, Joevarian
    Jeronimus, Bertus F.
    Jiang, Ding-Yu
    Jovanovic, Veljko
    Kamenov, Zeljka
    Kende, Anna
    Keng, Shian-Ling
    Tra Thi Thanh Kieu
    Koc, Yasin
    Kovyazina, Kamila
    Kozytska, Inna
    Krause, Joshua
    PATTERNS, 2022, 3 (04):
  • [45] Supervised Machine Learning Approach to Identify Early Predictors of Poor Outcome in Patients with COVID-19 Presenting to a Large Quaternary Care Hospital in New York City
    Zucker, Jason
    Gomez-Simmonds, Angela
    Purpura, Lawrence J.
    Shoucri, Sherif
    LaSota, Elijah
    Morley, Nicholas E.
    Sovic, Brit W.
    Castellon, Marvin A.
    Theodore, Deborah A.
    Bartram, Logan L.
    Miko, Benjamin A.
    Scherer, Matthew L.
    Meyers, Kathrine A.
    Turner, William C.
    Kelly, Maureen
    Pavlicova, Martina
    Basaraba, Cale N.
    Baldwin, Matthew R.
    Brodie, Daniel
    Burkart, Kristin M.
    Bathon, Joan
    Uhlemann, Anne-Catrin
    Yin, Michael T.
    Castor, Delivette
    Sobieszczyk, Magdalena E.
    JOURNAL OF CLINICAL MEDICINE, 2021, 10 (16)
  • [46] Fundamental ratios as predictors of ESG scores: a machine learning approach
    D'Amato, Valeria
    D'Ecclesia, Rita
    Levantesi, Susanna
    DECISIONS IN ECONOMICS AND FINANCE, 2021, 44 (02) : 1087 - 1110
  • [47] Fundamental ratios as predictors of ESG scores: a machine learning approach
    Valeria D’Amato
    Rita D’Ecclesia
    Susanna Levantesi
    Decisions in Economics and Finance, 2021, 44 : 1087 - 1110
  • [48] Financial predictors of firms' diversity scores: a machine learning approach
    Koseoglu, Mehmet Ali
    Arici, Hasan Evrim
    Saydam, Mehmet Bahri
    Olorunsola, Victor Oluwafemi
    EQUALITY DIVERSITY AND INCLUSION, 2025,
  • [49] A Machine Learning Approach to Determine the Predictors For Fatigue in Multiple Sclerosis
    Yalcin, Gizem Yagmur
    Toran, Meryem Kocaslan
    Ozgur, Su
    Toygar, Ismail
    Kurtuncu, Murat
    MULTIPLE SCLEROSIS JOURNAL, 2024, 30 (03) : 1038 - 1038
  • [50] Predictors of Dementia in the Oldest Old A Novel Machine Learning Approach
    Jia, Yichen
    Chang, Chung-Chou H.
    Hughes, Tiffany F.
    Jacobsen, Erin
    Wang, Shu
    Berman, Sarah B.
    Kamboh, M. Ilyas
    Ganguli, Mary
    ALZHEIMER DISEASE & ASSOCIATED DISORDERS, 2020, 34 (04): : 325 - 332