The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors

Artificial Intelligence in Medicine

Volumn 40, Issue 3, 2007, Pages 211-221

  Rowan, Michael ^a   Ryan, Thomas ^a   Hegarty, Francis ^a   O'Hare, Neil ^a  

^a ST JAMES S HOSPITAL   (Ireland)

Author keywords

Artificial neural networks; Length of stay prediction; Outcome prediction; Postoperative cardiac surgical patients

Indexed keywords

CARDIAC PATIENTS; OUTCOME PREDICTION; POSTOPERATIVE CARDIAC SURGICAL PATIENTS; STAY PREDICTION;

CARDIOLOGY; CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; INTENSIVE CARE UNITS; PATIENT MONITORING; SOFTWARE PACKAGES;

NEURAL NETWORKS;

ACCURACY; ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CARDIAC PATIENT; CLASSIFICATION; COMPUTER PROGRAM; CONTROLLED STUDY; FEMALE; HEART INFARCTION; HUMAN; INTENSIVE CARE UNIT; LENGTH OF STAY; MAJOR CLINICAL STUDY; MALE; POSTOPERATIVE PERIOD; PREDICTION; PREOPERATIVE PERIOD; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROGNOSIS; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; RISK FACTOR; SENSITIVITY AND SPECIFICITY; STATISTICAL SIGNIFICANCE;

AGED; AGED, 80 AND OVER; CARDIAC SURGICAL PROCEDURES; FEMALE; HUMANS; INTENSIVE CARE UNITS; LENGTH OF STAY; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); POSTOPERATIVE CARE; PREOPERATIVE CARE; RISK ASSESSMENT;

EID: 34447273327     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2007.04.005     Document Type: Article

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