Go green! Reusing brain monitoring data containing missing values: a feasibility study with traumatic brain injury patients.
BACKGROUND
Despite the wealth of information carried, periodic brain monitoring data are often incomplete with a significant amount of missing values. Incomplete monitoring data are usually discarded to ensure purity of data. However, this approach leads to the loss of statistical power, potentially biased study and a great waste of resources. Thus, we propose to reuse incomplete brain monitoring data by imputing the missing values - a green solution! To support our proposal, we have conducted a feasibility study to investigate the reusability of incomplete brain monitoring data based on the estimated imputation error.


METHODS
Seventy-seven patients, who underwent invasive monitoring of ICP, MAP, PbtO (2) and brain temperature (BTemp) for more than 24 consecutive hours and were connected to a bedside computerized system, were selected for the study. In the feasibility study, the imputation error is experimentally assessed with simulated missing values and 17 state-of-the-art predictive methods. A framework is developed for neuroclinicians and neurosurgeons to determine the best re-usage strategy and predictive methods based on our feasibility study.


CONCLUSIONS
The monitoring data of MAP and BTemp are more reliable for reuse than ICP and PbtO (2); and, for ICP and PbtO (2) data, a more cautious re-usage strategy should be employed. We also observe that, for the scenarios tested, the lazy learning method, K-STAR, and the tree-based method, M5P, are consistently 2 of the best among the 17 predictive methods investigated in this study.
Institute for Infocomm Research, Singapore, Singapore. mfeng@i2r.a-star.edu.sg
Mengling
M
Feng
Liang Yu
LY
Loy
Feng
F
Zhang
Zhuo
Z
Zhang
Kuralmani
K
Vellaisamy
Pei Loon
PL
Chin
Cuntai
C
Guan
Liang
L
Shen
Nicolas K K
NK
King
Kah Keow
KK
Lee
Beng Ti
BT
Ang
D000328:Adult; D000368:Aged; D015982:Bias; D001794:Blood Pressure; D001831:Body Temperature / Q000502:physiology; D001921:Brain / Q000378:metabolism / Q000503:physiopathology*; D001930:Brain Injuries / Q000473:pathology*; D002648:Child; D003627:Data Interpretation, Statistical; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007427:Intracranial Pressure / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D010100:Oxygen / Q000378:metabolism; D012189:Retrospective Studies; D060388:Support Vector Machine; D055815:Young Adult
D010100:Oxygen