Classifying mood disorders using multiple kernel learning on multimodal neuroimaging data: translating biological data into a diagnostic tool for depression

被引：0
|
作者：
Vai, B. [1 ]
Parenti, L. [1 ]
Cara, C. [1 ]
Verga, C. [1 ]
Bollettini, I. [1 ]
Poletti, S. [1 ]
Colombo, C. [1 ]
Benedetti, F. [1 ]
机构：
[1] IRCCS Osped San Raffaele, Neurosci, Milan, Italy
来源：
EUROPEAN NEUROPSYCHOPHARMACOLOGY | 2019年 / 29卷
关键词：
D O I：
10.1016/j.euroneuro.2019.09.095
中图分类号：
R74 [神经病学与精神病学];
学科分类号：
摘要：
P.029
引用
收藏
页码：S40 / S41
页数：2
相关论文
共 50 条
  • [41] Learning from data streams using kernel least-mean-square with multiple kernel-sizes and adaptive step-size
    Garcia-Vega, Sergio
    Zeng, Xiao-Jun
    Keane, John
    NEUROCOMPUTING, 2019, 339 : 105 - 115
  • [42] Integrating Biological Data Across Multiple Platforms Using Importance-Weighted Transfer Learning and Applications to Breast Cancer Data Sets
    Achanta, Hema K.
    Misganaw, Burook
    Vidyasagar, M.
    2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017), 2017, : 955 - 960
  • [43] Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study
    Hilbert, Kevin
    Lueken, Ulrike
    Muehlhan, Markus
    Beesdo-Baum, Katja
    BRAIN AND BEHAVIOR, 2017, 7 (03):
  • [44] Integration of Drug Perturbed Dose Dependent Response Data in Predictive Computational Models of Chemotherapeutic Response Using Multiple-Kernel-Learning
    Mannheimer, Joshua
    Prasad, Ashok
    Gustafson, Daniel
    FASEB JOURNAL, 2020, 34
  • [45] Approach to Explosive Hazard Detection Using Sensor Fusion and Multiple Kernel Learning with Downward-Looking GPR and EMI Sensor Data
    Pinar, Anthony
    Masarik, Matthew
    Havens, Timothy C.
    Burns, Joseph
    Thelen, Brian
    Becker, John
    DETECTION AND SENSING OF MINES, EXPLOSIVE OBJECTS, AND OBSCURED TARGETS XX, 2015, 9454
  • [46] Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study
    Delgado-Garcia, Guillermo
    Engbers, Jordan D. T.
    Wiebe, Samuel
    Mouches, Pauline
    Amador, Kimberly
    Forkert, Nils D.
    White, James
    Sajobi, Tolulope
    Klein, Karl Martin
    Josephson, Colin B.
    EPILEPSIA, 2023, 64 (10) : 2781 - 2791
  • [47] An empirical comparison of three methods for multiple cutoff diagnostic test meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool using published data vs individual level data
    Benedetti, Andrea
    Levis, Brooke
    Ruecker, Gerta
    Jones, Hayley E.
    Schumacher, Martin
    Ioannidis, John P. A.
    Thombs, Brett
    RESEARCH SYNTHESIS METHODS, 2020, 11 (06) : 833 - 848
  • [48] Classifying Personality Traits from Text Data: A Machine Learning Approach Using Stochastic Gradient Descent for Simplified Jungian Typology-Based Assessment Tool
    Muliawati, Tri Hadiah
    Swandaru, Tina Rumy
    Kusumaningtyas, Entin Martiana
    Bimantoko, Iqbal
    2024 INTERNATIONAL ELECTRONICS SYMPOSIUM, IES 2024, 2024, : 528 - 533
  • [49] An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data
    Zelalem F. Negeri
    Brooke Levis
    John P. A. Ioannidis
    Brett D. Thombs
    Andrea Benedetti
    BMC Medical Research Methodology, 24
  • [50] An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data
    Negeri, Zelalem F.
    Levis, Brooke
    Ioannidis, John P. A.
    Thombs, Brett D.
    Benedetti, Andrea
    BMC MEDICAL RESEARCH METHODOLOGY, 2024, 24 (01)
12345