Data Mining in Neurology

[1]  Elizabeth A Madigan,et al.  A data mining approach in home healthcare: outcomes and service use , 2006, BMC Health Services Research.

[2]  Janaina Mourão Miranda,et al.  Unsupervised analysis of fMRI data using kernel canonical correlation , 2007, NeuroImage.

[3]  B. L. Davies,et al.  Automation of colonoscopy. II. Visual control aspects , 1998 .

[4]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[5]  B. Jennett,et al.  ASSESSMENT OF OUTCOME AFTER SEVERE BRAIN DAMAGE A Practical Scale , 1975, The Lancet.

[6]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[7]  Yan Zhu,et al.  Computerized tumor boundary detection using a Hopfield neural network , 1997, IEEE Transactions on Medical Imaging.

[8]  Ivan Bratko,et al.  KARDIO - a study in deep and qualitative knowledge for expert systems , 1989 .

[9]  Karina Gibert,et al.  Knowledge Discovery about Quality of Life Changes of Spinal Cord Injury Patients: Clustering Based on Rules by States , 2009, MIE.

[10]  E. Steyerberg,et al.  Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. , 2007, Journal of neurotrauma.

[11]  A. Tutaj,et al.  Prediction of severe brain damage outcome using two data mining methods , 2008, 2008 Conference on Human System Interactions.

[12]  Ewout W Steyerberg,et al.  Prediction of Outcome in Traumatic Brain Injury with Computed Tomographic Characteristics: A Comparison between the Computed Tomographic Classification and Combinations of Computed Tomographic Predictors , 2005, Neurosurgery.

[13]  Abraham Z. Snyder,et al.  A default mode of brain function: A brief history of an evolving idea , 2007, NeuroImage.

[14]  Jeff H. Duyn,et al.  Modulation of spontaneous fMRI activity in human visual cortex by behavioral state , 2009, NeuroImage.

[15]  Fong Chan,et al.  Service patterns related to successful employment outcomes of persons with traumatic brain injury in vocational rehabilitation. , 2007, NeuroRehabilitation.

[16]  George D. Magoulas,et al.  Machine Learning in Medical Applications , 2001, Machine Learning and Its Applications.

[17]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[18]  Antonio Candelieri,et al.  A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients , 2010, The open medical informatics journal.

[19]  G. Rees,et al.  Neuroimaging: Decoding mental states from brain activity in humans , 2006, Nature Reviews Neuroscience.

[20]  W. Sannita,et al.  Residual emotional processing in the vegetative state: A scientific issue? , 2011, Clinical Neurophysiology.

[21]  D. Conforti,et al.  Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study , 2010, Clinical Neurophysiology.

[22]  B. Biswal,et al.  Simultaneous assessment of flow and BOLD signals in resting‐state functional connectivity maps , 1997, NMR in biomedicine.

[23]  L. Iezzoni.  Risk Adjusting Rehabilitation Outcomes: An Overview of Methodologic Issues , 2004, American journal of physical medicine & rehabilitation.

[24]  B. Jennett,et al.  Assessment of coma and impaired consciousness. A practical scale. , 1974, Lancet.

[25]  G. Teasdale,et al.  Glasgow Head Injury Outcome Prediction Program: an independent assessment , 1999, Journal of neurology, neurosurgery, and psychiatry.

[26]  Arthur Gretton,et al.  Comparison of Pattern Recognition Methods in Classifying High-resolution Bold Signals Obtained at High Magnetic Field in Monkeys , 2008 .

[27]  A. Candelieri,et al.  Early detection of decompensation conditions in heart failure patients by knowledge discovery: The HEARTFAID approaches , 2008, 2008 Computers in Cardiology.

[28]  A. Marmarou,et al.  Prediction tree for severely head-injured patients. , 1991, Journal of neurosurgery.

[29]  Jau-Min Wong,et al.  A Knowledge Discovery Approach to Diagnosing Intracranial Hematomas on Brain CT: Recognition, Measurement and Classification , 2008, ICMB.

[30]  M. Schummer,et al.  Comparison of Breast Cancer to Healthy Control Tissue Discovers Novel Markers with Potential for Prognosis and Early Detection , 2010, PloS one.

[31]  V. Lagani,et al.  Clinical signs and early prognosis in vegetative state: A decisional tree, data-mining study , 2008, Brain injury.

[32]  Antonio Candelieri,et al.  Knowledge Discovery Approaches for Early Detection of Decompensation Conditions in Heart Failure Patients , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[33]  Rebecca Smith,et al.  A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries , 2009, BMC Medical Informatics Decis. Mak..

[34]  M A Foulkes,et al.  The diagnosis of head injury requires a classification based on computed axial tomography. , 1992, Journal of neurotrauma.

[35]  Antonio Candelieri,et al.  Data Mining and the Functional Relationship between Heart Rate Variability and Emotional Processing - Comparative Analyses, Validation and Application , 2010, HEALTHINF.

[36]  D. Conforti,et al.  Heart Rate Response to Music An Artificial Intelligence Study on Healthy and Traumatic Brain-Injured Subjects , 2008 .

[37]  Knowledge discovery: Detecting elderly patients with impaired mobility. , 2006, Studies in health technology and informatics.

[38]  Robert Ivor John,et al.  Application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification , 1997, Artif. Intell. Medicine.

[39]  Antonio Candelieri,et al.  Vegetative State: Early Prediction of Clinical Outcome by Artificial Neural Network , 2009 .

[40]  Andreas Bartels,et al.  Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity , 2009, NIPS.

[41]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[42]  Colin Campbell,et al.  Image processing and neural computing used in the diagnosis of tuberculosis , 1998 .