TY  - JOUR
PY  - 2018//
TI  - The trail making test: a study of its ability to predict falls in the acute neurological in-patient population
JO  - Clinical rehabilitation
A1  - Mateen, Bilal Akhter
A1  - Bussas, Matthias
A1  - Doogan, Catherine
A1  - Waller, Denise
A1  - Saverino, Alessia
A1  - Király, Franz J.
A1  - Playford, E. Diane
SP  - 1396
EP  - 1405
VL  - 32
IS  - 10
N2  - OBJECTIVE: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. <br><br>DESIGN: Prospective cohort study. SETTING: Tertiary neurological and neurosurgical center. SUBJECTS: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. MAIN MEASURES: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). <br><br>RESULTS: The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. <br><br>CONCLUSION: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.<p />  <p>Language: en</p>
LA  - en
SN  - 0269-2155
UR  - http://dx.doi.org/10.1177/0269215518771127
ID  - ref1
ER  -