Unsupervised risk stratification in clinical datasets: Identifying patients at risk of rare outcomes

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 1023-1030

  Syed, Zeeshan ^a   Rubinfeld, Ilan ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b HENRY FORD HEALTH SYSTEM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE CORONARY SYNDROMES; ADDITIONAL KNOWLEDGE; ADVERSE OUTCOMES; CLINICAL CONDITIONS; CLINICAL RISKS; DATA SETS; EXPERT KNOWLEDGE; FEATURE SPACE; HIGH-RISK PATIENTS; LABELED TRAINING DATA; MACHINE LEARNING METHODS; MINIMUM ENCLOSING BALL; RISK STRATIFICATION; SURGICAL PROCEDURES; TRAINING EXAMPLE; UNSUPERVISED ANOMALY DETECTION;

LEARNING ALGORITHMS; LEARNING SYSTEMS; POPULATION STATISTICS;

DATA ACQUISITION;

EID: 77956515027     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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