Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection

Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Volumn , Issue , 2015, Pages 343-350

  Henriksson, Aron ^a   Zhao, Jing ^a   Bostrom, Henrik ^a   Dalianis, Hercules ^a  

^a STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; RECORDS MANAGEMENT; ROBUSTNESS (CONTROL SYSTEMS); SEMANTICS;

CO-OCCURRENCE INFORMATIONS; DISTRIBUTIONAL SEMANTICS; ELECTRONIC HEALTH RECORD; ELECTRONIC HEALTH RECORD (EHRS); HIGH-DIMENSIONAL; NATURAL LANGUAGE PROCESSING; PREDICTIVE MODELS; PREDICTIVE PERFORMANCE;

VECTOR SPACES;

EID: 84962421735     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2015.7359705     Document Type: Conference Paper

References (37)

1. B. Sibbald, " Rofecoxib (vioxx) voluntarily withdrawn from market, " Canadian Medical Association Journal, vol. 171, no. 9, pp. 1027-1028, 2004 .
2. C. D. Furberg and B. Pitt, "Withdrawal of cerivastatin from the world market, " Curr Control Trials Cardiovasc Med, vol. 2, no. 5, pp. 205-207, 2001 .
3. R. Howard, A. Avery, S. Slavenburg, S. Roy al, G. Pipe, P. Lucassen, and M. Pirmohamed, "Which drugs cause preventable admissions to hospital? a systematic review, " British Journal of Clinical Pharmacology, vol. 63, no. 2, pp. 136-147, 2007 .
4. L. Hazell and S. A. Shakir, " Under-reporting of adverse drug reactions, " Drug Safety, vol. 29, no. 5, pp. 385-396, 2006 .
5. A. J. Forster, A. Jennings, C. Chow, C. Leeder, and C. van Walraven, " A sy stematic review t o evaluate the accuracy o f electronic adverse drug event detection, " JAMIA, vol. 19, no. 1, pp. 31-38, 2012 .
6. R. Harpaz, W. DuMouchel, N. H. Shah, D. Madigan, P. Ryan, and C. Friedman, "Novel data-mining methodologies for adverse drug event discovery and analysis, " Clinical Pharmacology & Therapeutics, vol. 91, no. 6, pp. 1010-1021, 2012 .
7. J. Zhao, A. Henriksson, and H. Bostrom, " Detecting adverse drug events using concept hierarchies of clinical codes, " in Proceedings of IEEE International Conference on Healthcare Informatics. IEEE, 2014, pp. 285-293 .
8. J. Zhao, A. Henriksson, L. Asker, and H. Bostrom, " Detecting adverse drug events w ith multiple representations of clinical measurements, " in Proceedings of IEEE International Conference on BioInformatics and Biomedicine. IEEE, 2014, pp. 536-543 .
9. J. Zhao, A. Henriksson, and H. Bostrom, "Cascading adverse drug event detection in electronic health records, " in Proceedings of IEEE International Conference on Data Science and Advanced Analytics. IEEE, 2015 .
10. A. Henriksson, M. Kvist, H. Dalianis, and M. Duneld, " Identify ing adverse drug event information in clinical notes w ith distributional semantic representations of context, " Journal of Biomedical Informatics, vol. 57, pp. 333-349 .
11. A. Henriksson, J. Zhao, H. Bostrom, and H. Dalianis, "Modeling heterogeneous clinical seq uence data in semantic space for adverse drug event detection, " in Proceedings of IEEE International Conference on Data Science and Advanced Analytics. IEEE, 2015 .
12. M. Sahlgren, " The word-space model: using distributional analysis to represent sy ntagmatic and paradigmatic relations betw een w ords in high-dimensional vector spaces, " PhD Thesis, S tockholm University, 2006 .
13. G. Lapesa, S. Evert, and S. S. M Walde, "Contrasting sy ntagmatic and paradigmatic relations: Insights from distributional semantic models, " in Proceedings of the Third Joint Conference on Lexical and Computational Semantics (∗SEM 2014), 2014, pp. 160-170 .
14. G. Lapesa and S. Evert, "A large scale evaluation of distributional semantic models: parameters, interactions and model selection, " Transactions of the Association for Computational Linguistics, vol. 2, pp. 531-545, 2014.
15. T. G. Dietterich, " Ensemble methods in machine learning;' in Multiple Classifier Systems. Springer, 2000, pp. 1-15 .
16. Z. S. Harris, " Distributional structure." Word, 1954 .
17. T. K. Landauer and S. T. Dumais, "A solution to plato' s problem: The latent semantic analysis theory of acq uisition, induction, and representation of knowledge." Psychological review, vol. 104, no. 2, p. 211, 1997 .
18. P. D. Turney, P. Pantel et al., "From freq uency to meaning: Vector space models of semantics, " Journal of Artificial Intelligence Research, vol. 37, no. 1, pp. 141-188, 2010 .
19. T. Cohen and D. Widdows, " Empirical distributional semantics: methods and biomedical applications, " Journal of Biomedical Informatics, vol. 42, no. 2, pp. 390-405, 2009 .
20. A. Henriksson, " S emantic spaces of clinical text: leveraging distributional semantics for natural language processing of electronic health records, " Licentiate Thesis, S tockholm University, 2013 .
21. A. Henriksson, H. Moen, M. S keppstedt, Y. Daudaravicius, and M. Duneld, " S y nony m extraction and abbreviation expansion w ith ensembles of semantic spaces, " Journal of Biomedical Semantics, vol. 5, no. 6, pp. 1-25, 2014 .
22. A. Henriksson, H. Dalianis, and S. Kow alski, "Generating features for named entity recognition by learning prototy pes in semantic space: The case of de-identify ing health records, " in Proceedings of IEEE International Conference on BioInformatics and Biomedicine. IEEE, 2014, pp. 450-457 .
23. A. Henriksson, "Learning multiple distributed prototy pes of semantic categories for named entity recognition, " International Journal of Data Mining and BioInformatics, vol. 13, no. 4, pp. 395-411, 2015 .
24. D. Austen-S mith and J. S. Banks, " Information aggregation, rationality, and the condorcet j ury theorem, " American Political Science Review, vol. 90, no. 1, pp. 34-45, 1996 .
25. L. J. K uncheva and C. J. Whitaker, "Measures of diversity in classifier ensembles and their relationship w ith the ensemble accuracy, " Machine Learning, vol. 51, no. 2, pp. 181-207, 2003 .
26. A. K rogh and J. Vedelsby, "Neural netw ork ensembles, cross validation, and cctive learning, " in Advances in Neural Information Processing Systems. MIT Press, 1995, pp. 231-238 .
27. H. Dalianis, M. Hassel, A. Henriksson, and M. S keppstedt, " S tockholm EPR Corpus: a clinical database used to improve health care, " in Swedish Language Technology Conference, 2012 .
28. J. S tausberg and J. Hasford, " Drug-related admissions and hospitalacq uired adverse drug events in germany: a longitudinal analysis from 2003 to 2007 of icd-10-coded routine data, " BMC Health Services Research, vol. 11, no. 1, p. 134, 2011 .
29. R. O stling, " S tagger: an open-source part of speech tagger for sw edish, " Northern European Journal of Language Technology (NEJLT), vol. 3, pp. 1-18, 2013 .
30. T. Mikolov, K. Chen, G. Corrado, and J. Dean, " Efficient estimation of w ord representations in vector space, " in ICLR Worshop, 2013 .
31. M. Baroni, G. Dinu, and G. K ruszewski, " Don' t count, predict! a sy stematic comparison of context-counting vs. context-predicting semantic vectors, " in Association for Computational Linguistics, vol. 1, 2014, pp. 238-247 .
32. Y. Goldberg and O. Levy, " w ord2 vec explained: deriving mikolov et al.' s negative-sampling w ord-embedding method, " arXiv preprint arXiv:1402. 3722, 2014 .
33. L. Breiman, J. Friedman, C. J. S tone, and R. A. Olshen, Classification and regression trees. CRC press, 1984 .
34. L. Rokach, " Ensemble-based classifiers, " Artificial Intelligence Review, vol. 33, no. 1-2, pp. 1-39, 2010 .
35. L. Breiman, " Random forests, " Machine Learning, vol. 45, no. 1, pp. 5-32, 2001 .
36. J. Dems ar, " S tatistical comparisons of classifiers over multiple data sets, " The Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006 .
37. H. Bostrom, "Feature vs. classifier fusion for predictive data mining-a case study in pesticide classification, " in International Conference on Information Fusion, 2007, pp. 121-126.