Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI)
暂无分享,去创建一个
Myra Spiliopoulou | José M. Peña | Georg Krempl | Fernando Maestú | Zaigham Faraz Siddiqui | Nuria Paul
[1] João Gama,et al. A framework to monitor clusters evolution applied to economy and finance problems , 2012, Intell. Data Anal..
[2] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[3] Padhraic Smyth,et al. Trajectory clustering with mixtures of regression models , 1999, KDD '99.
[4] David F. Garway-Heath,et al. The Pseudotemporal Bootstrap for Predicting Glaucoma From Cross-Sectional Visual Field Data , 2010, IEEE Transactions on Information Technology in Biomedicine.
[5] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[6] Enrique J. Gómez,et al. Data mining applied to the cognitive rehabilitation of patients with acquired brain injury , 2013, Expert Syst. Appl..
[7] Myra Spiliopoulou,et al. FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments , 2012, Int. J. Data Warehous. Min..
[8] Yuanxi Li,et al. Modelling and analysing the dynamics of disease progression from cross-sectional studies , 2013, J. Biomed. Informatics.
[9] Allen W. Brown,et al. Clinical elements that predict outcome after traumatic brain injury: a prospective multicenter recursive partitioning (decision-tree) analysis. , 2005, Journal of neurotrauma.
[10] Nilam Ram,et al. Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups , 2009, International journal of behavioral development.
[11] Myra Spiliopoulou,et al. Are Some Brain Injury Patients Improving More Than Others? , 2014, Brain Informatics and Health.
[12] J. Ware,et al. Applied Longitudinal Analysis , 2004 .
[13] Hon-Yi Shi,et al. In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models. , 2013, Journal of neurosurgery.
[14] Myra Spiliopoulou,et al. Summarizing Cluster Evolution in Dynamic Environments , 2011, ICCSA.
[15] B. Litz,et al. Trajectories of response to treatment for posttraumatic stress disorder. , 2012, Behavior therapy.
[16] Vincent Corruble,et al. Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between decision tree analysis and logistic regression. , 2002, Journal of neurosurgery.
[17] Daniel S Nagin,et al. Group-based trajectory modeling in clinical research. , 2010, Annual review of clinical psychology.
[18] Lisa Koski,et al. Identifying and Characterizing Trajectories of Cognitive Change in Older Persons with Mild Cognitive Impairment , 2011, Dementia and Geriatric Cognitive Disorders.
[19] Myra Spiliopoulou,et al. Online Clustering of High-Dimensional Trajectories under Concept Drift , 2011, ECML/PKDD.
[20] D.,et al. Regression Models and Life-Tables , 2022 .
[21] Myra Spiliopoulou,et al. MONIC: modeling and monitoring cluster transitions , 2006, KDD '06.
[22] Myra Spiliopoulou,et al. Where Are We Going? Predicting the Evolution of Individuals , 2012, IDA.
[23] Anthony Fabio,et al. Group-based trajectory analysis applications for prognostic biomarker model development in severe TBI: a practical example. , 2013, Journal of neurotrauma.
[24] Fernando Maestú,et al. Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury. , 2010, Brain : a journal of neurology.
[25] A. Rovlias,et al. Classification and regression tree for prediction of outcome after severe head injury using simple clinical and laboratory variables. , 2004, Journal of neurotrauma.