Clustering people with multiple sclerosis based on machine learning techniques applied to the biomechanical and clinical assessment of the disease main physical symptoms

被引：0

作者：

Barbado, David ^{[1
,2
]}

Alcantara-Solis, Alberto ^{[1
]}

Carpena, Carmen ^{[1
,2
]}

Valero-Conesa, Gregori ^{[1
]}

Miralles-Borras, Aaron ^{[1
]}

Rios-Calonge, Javier De Los ^{[1
]}

Prat-Luri, Amaya ^{[1
]}

Caballero, Carla ^{[1
]}

Perez-Sempere, Angel ^{[2
,3
]}

Vera-Garcia, Francisco ^{[1
,2
]}

机构：

[1] Miguel Hernandez Univ, Sport Sci Dept, Sport Res Ctr, Elche, Spain

[2] Inst Hlth & Biomed Res ISABIAL Fdn, Neurosci Grp, Alicante, Spain

[3] Miguel Hernandez Univ, Dept Clin Med, San Juan, Spain

来源：

MULTIPLE SCLEROSIS JOURNAL | 2023年 / 29卷

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：

P1182/1485

引用

页码：758 / 759

页数：2

共 31 条

[1] Exploration of machine learning techniques in predicting multiple sclerosis disease course
Zhao, Yijun
Healy, Brian C.
Rotstein, Delia
Guttmann, Charles R. G.
Bakshi, Rohit
Weiner, Howard L.
Brodley, Carla E.
Chitnis, Tanuja
[J]. PLOS ONE, 2017, 12 (04):
[2] Predicting clinical conversion to multiple sclerosis in patients with clinically isolated syndrome using machine learning techniques
Ciccarelli, O.
Kwok, P. P.
Wottschel, V.
Chard, D.
Stromillo, M. L.
De Stefano, N.
Thompson, A. J.
Miller, D. H.
Alexander, D. H.
[J]. MULTIPLE SCLEROSIS JOURNAL, 2012, 18 : 30 - 31
[3] Machine learning classifier to identify clinical and radiological features relevant to disease progression in multiple sclerosis
Tommasin, S.
Taloni, A.
Farrelly, F. A.
Petsas, N.
Ruggieri, S.
Gianni, C.
De Giglio, L.
Pozzilli, C.
Pantano, P.
[J]. MULTIPLE SCLEROSIS JOURNAL, 2019, 25 : 467 - 468
[4] Modeling Subject-Level Disease Progression for Multiple Sclerosis Clinical Trials with Machine Learning
Walsh, Jonathan
Smith, Aaron
Pouliot, Yannick
Li-Bland, David
Loukianov, Anton
Fisher, Charles
[J]. NEUROLOGY, 2020, 94 (15)
[5] Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis
Noteboom, Samantha
Seiler, Moritz
Chien, Claudia
Rane, Roshan P.
Barkhof, Frederik
Strijbis, Eva M. M.
Paul, Friedemann
Schoonheim, Menno M.
Ritter, Kerstin
[J]. JOURNAL OF NEUROLOGY, 2024, 271 (08) : 5577 - 5589
[6] CLUSTERING OF CLINICAL-ECHOCARDIOGRAPHIC PHENOTYPES OF COVID-19 DISEASE USING MACHINE-LEARNING TECHNIQUES
Hochstadt, Aviram
Shpigelman, Eran
Coster, Dan
Merdler, Ilan
Topilsky, Yan
Shamir, Ron
[J]. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2022, 79 (09) : 2146 - 2146
[7] Modeling subject-level disease progression for multiple sclerosis in clinical trials using machine learning
Walsh, J.
[J]. MULTIPLE SCLEROSIS JOURNAL, 2020, 26 (3_SUPPL) : 113 - 113
[8] Effects of Mindfulness-based interventions on physical symptoms in people with multiple sclerosis - a systematic review and meta-analysis
Simpson, Robert
Simpson, Sharon
Ramparsad, Nitish
Lawrence, Maggie
Booth, Jo
Mercer, Stewart W.
[J]. MULTIPLE SCLEROSIS AND RELATED DISORDERS, 2020, 38
[9] Machine Learning Models for Predicting Celiac Disease Based on Non-invasive Clinical Symptoms
Calin, Alina Delia
[J]. ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, PT I, AIAI 2024, 2024, 711 : 145 - 159
[10] Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques
Perez del Palomar, Amaya
Cegonino, Jose
Montolio, Alberto
Orduna, Elvira
Vilades, Elisa
Sebastian, Berta
Pablo, Luis E.
Garcia-Martin, Elena
[J]. PLOS ONE, 2019, 14 (05):

← 1 2 3 4 →