Feature importance analysis for patient management decisions

Studies in Health Technology and Informatics

Volumn 160, Issue PART 1, 2010, Pages 861-865

  Valko, Michal ^a   Hauskrecht, Milos ^a  

^a University of Pittsburgh   (United States)

Author keywords

Clinical; Data interpretation; Decision support systems; Decision support techniques; Evidence based medicine; Statistical

Indexed keywords

ARTIFICIAL INTELLIGENCE; LABORATORIES;

CLINICAL; DATA INTERPRETATION; DECISION SUPPORT TECHNIQUES; EVIDENCE-BASED MEDICINE; STATISTICAL;

DECISION SUPPORT SYSTEMS;

EID: 78649521047     PISSN: 09269630     EISSN: 18798365     Source Type: Conference Proceeding    
DOI: 10.3233/978-1-60750-588-4-861     Document Type: Conference Paper

References (8)

1. Valko M, Cooper G, Seybert A, Visweswaran S, Saul M, Hauskrecht M: Conditional anomaly detection methods for patient-management alert systems, ICML Workshop on Machine Learning in Health Care, 2008.
2. Valko M, Hauskrecht M: Distance metric learning for conditional anomaly detection, Twenty-First International Florida AI Research Society Conference, 2008.
3. Hauskrecht M, Valko M, Kveton B, Visweswaram S, Cooper G: Evidence-based Anomaly Detection in Clinical Domains. AMIA Conference, 2007.
4. Hauskrecht M, Valko M, Kveton B, Visweswaram S, Cooper G: Evidence-based Anomaly Detection in Clinical Domains. AMIA Conference, 2007.
5. Vapnik VN. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
6. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121-167. 1998
7. K. Morik, P. Brockhausen, T. Joachims. Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. In Proceedings of the Sixteenth international Conference on Machine Learning, pp. 268-277, 1999.
8. E. A. Balas EA. Information systems can prevent errors and improve quality. J Am Med Inform Assoc. Jul-Aug; 8(4):398-9, 2001