Outcome prediction for patients with severe traumatic brain injury using permutation entropy analysis of electronic vital signs data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7376 LNAI, Issue , 2012, Pages 415-426

  Kalpakis, Konstantinos ^a   Yang, Shiming ^a   Hu, Peter F ^b   MacKenzie, Colin F ^b   Stansbury, Lynn G ^b   Stein, Deborah M ^b   Scalea, Thomas M ^b  

^a University of Marylandw Baltimore County   (United States)

^b UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONALLY EFFICIENT; DATA SETS; DYNAMIC BEHAVIORS; EARLY PREDICTION; LEARNING METHODS; LEAVE-ONE-OUT CROSS VALIDATIONS; OUTCOME PREDICTION; PERMUTATION ENTROPY; PREDICTION ACCURACY; RECEIVER OPERATING CHARACTERISTIC ANALYSIS; SENSITIVITY AND SPECIFICITY; TRAUMATIC BRAIN INJURIES; VITAL SIGN;

BRAIN; DATA MINING; ENTROPY; HOSPITALS; LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION;

FORECASTING;

EID: 84864936878     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31537-4_33     Document Type: Conference Paper

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