Power of breathing determined noninvasively with use of an artificial neural network in patients with respiratory failure

Critical Care Medicine

Volumn 34, Issue 4, 2006, Pages 1052-1059

  Banner, Michael J ^a   Euliano, Neil R ^b   Brennan, Vic ^b   Peters, Carl ^a   Layon, A Joseph ^a   Gabrielli, Andrea ^a  

^a UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

^b Convergent Engineering ^*  (United States)

Author keywords

Acute respiratory distress syndrome; Artificial neural networks; Computers; Esophageal pressure; Mechanical ventilators; Power of breathing; Respiratory monitoring; Respiratory muscles; Work of breathing

Indexed keywords

ACCURACY; ADULT; AIRWAY PRESSURE; ARTICLE; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL VENTILATION; BREATHING; BREATHING MUSCLE; CATHETER; CLINICAL ARTICLE; CLINICAL PRACTICE; CONTROLLED STUDY; CORRELATION ANALYSIS; ESOPHAGUS; ESOPHAGUS INTUBATION; FEMALE; HUMAN; INTENSIVE CARE UNIT; INTUBATION; MALE; MATHEMATICAL COMPUTING; PREDICTION; PRESSURE SUPPORT VENTILATION; PRIORITY JOURNAL; RESPIRATORY FAILURE; TREATMENT OUTCOME; UNIVERSITY HOSPITAL;

FEMALE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PROSPECTIVE STUDIES; RESPIRATION; RESPIRATION, ARTIFICIAL; RESPIRATORY FUNCTION TESTS; RESPIRATORY INSUFFICIENCY;

EID: 33645818376     PISSN: 00903493     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.CCM.0000206288.90613.1C     Document Type: Article

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