Learning to identify locally actionable health anomalies

AAAI Spring Symposium - Technical Report

Volumn SS-10-01, Issue , 2010, Pages 120-121

  Chen, Kuang ^a   Brunskill, Emma ^a   Dick, Jonathan ^b   Dhadialla, Prabhjot ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

^c Center for International Forestry Research   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; HEALTH; VISUALIZATION;

ADAPTIVE TOOLS; DATA QUALITY; DECISION MAKING PROCESS; DEVELOPING REGIONS; EVIDENCE- BASED DECISIONS; LOCAL INFORMATION; RURAL HEALTH SYSTEMS; SERVICE DELIVERY;

DECISION MAKING;

EID: 77957962309     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. Joachims, T. 1999. Transductive inference for text classification using support vector machines. In ICML.
2. Kapoor, A., and Horvitz, E. 2009. Breaking boundaries: Active information acquisition across learning and diagnosis. In NIPS.
3. Mackay, K. 2009. How to Build M&E Systems to Support Better Government. Washington, DC: World Bank.
4. Tong, S., and Koller, D. 2001. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research 45-66.