Predicting adverse drug events from electronic medical records
Publication
, Journal Article
Davis, J; Costa, VS; Peissig, P; Caldwell, M; Page, D
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2015
Learning from electronic medical records (EMR) poses many challenges from a knowledge representation point of view. This chapter focuses on how to cope with two specific challenges: the relational nature of EMRs and the uncertain dependence between a patient’s past and future health status. We discuss three different approaches for allowing standard propositional learners to incorporate relational information. We evaluate these approaches on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication.
Duke Scholars
Published In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
January 1, 2015
Volume
9521 LNCS
Start / End Page
243 / 257
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
Citation
APA
Chicago
ICMJE
MLA
NLM
Davis, J., Costa, V. S., Peissig, P., Caldwell, M., & Page, D. (2015). Predicting adverse drug events from electronic medical records. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9521 LNCS, 243–257. https://doi.org/10.1007/978-3-319-28007-3_16
Davis, J., V. S. Costa, P. Peissig, M. Caldwell, and D. Page. “Predicting adverse drug events from electronic medical records.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9521 LNCS (January 1, 2015): 243–57. https://doi.org/10.1007/978-3-319-28007-3_16.
Davis J, Costa VS, Peissig P, Caldwell M, Page D. Predicting adverse drug events from electronic medical records. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015 Jan 1;9521 LNCS:243–57.
Davis, J., et al. “Predicting adverse drug events from electronic medical records.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9521 LNCS, Jan. 2015, pp. 243–57. Scopus, doi:10.1007/978-3-319-28007-3_16.
Davis J, Costa VS, Peissig P, Caldwell M, Page D. Predicting adverse drug events from electronic medical records. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015 Jan 1;9521 LNCS:243–257.
Published In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
January 1, 2015
Volume
9521 LNCS
Start / End Page
243 / 257
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences