A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2015, Pages 446-453

  Ghassemi, Marzyeh ^a   Pimentel, Marco A F ^b   Brennan, Thomas ^a   Clifton, David A ^a   Szolovits, Peter ^b   Naumann, Tristan ^a   Feng, Mengling ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BLOOD PRESSURE; BRAIN; DISEASES; FORECASTING;

CLASSIFICATION FEATURES; CLINICAL DATA SETS; INTRA-CRANIAL PRESSURE; MEAN ARTERIAL BLOOD PRESSURES; PHYSIOLOGICAL SIGNALS; SIGNAL INTERPOLATION; TIME SERIES MODELS; TRAUMATIC BRAIN INJURIES;

PHYSIOLOGICAL MODELS;

EID: 84959548610     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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