Are Some Brain Injury Patients Improving More Than Others?

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progress of a disease and in the evolution of individuals subjected to treatment. We investigate the evolution of patients on the basis of medical tests before and during treatment after brain trauma: we want to understand how similar patients can become to healthy participants. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patient subpopulations, dealing with the above challenges. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls.

[1]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[2]  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.

[3]  Enrique J. Gómez,et al.  Data mining applied to the cognitive rehabilitation of patients with acquired brain injury , 2013, Expert Syst. Appl..

[4]  Myra Spiliopoulou,et al.  Summarizing Cluster Evolution in Dynamic Environments , 2011, ICCSA.

[5]  Myra Spiliopoulou,et al.  FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments , 2012, Int. J. Data Warehous. Min..

[6]  Yuanxi Li,et al.  Modelling and analysing the dynamics of disease progression from cross-sectional studies , 2013, J. Biomed. Informatics.

[7]  Myra Spiliopoulou,et al.  Where Are We Going? Predicting the Evolution of Individuals , 2012, IDA.

[8]  João Gama,et al.  A framework to monitor clusters evolution applied to economy and finance problems , 2012, Intell. Data Anal..

[9]  Frank Klawonn,et al.  Advances in Intelligent Data Analysis XI , 2012, Lecture Notes in Computer Science.

[10]  D.,et al.  Regression Models and Life-Tables , 2022 .

[11]  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.

[12]  Josh Bongard,et al.  C L I N I C a L a R T I C L E , 2022 .

[13]  Myra Spiliopoulou,et al.  MONIC: modeling and monitoring cluster transitions , 2006, KDD '06.

[14]  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.

[15]  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.

[16]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[17]  Fernando Maestú,et al.  Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury. , 2010, Brain : a journal of neurology.

[18]  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.

[19]  David Taniar,et al.  Computational Science and Its Applications - ICCSA 2011 , 2011, Lecture Notes in Computer Science.

[20]  J. Ware,et al.  Applied Longitudinal Analysis , 2004 .