Archive

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance

Journal: Applied Clinical Informatics
ISSN: 1869-0327
DOI: https://doi.org/10.4338/ACI-2016-12-RA-0211
Issue: Vol. 8: Issue 2 2017
Pages: 560-580
Ahead of Print: 2017-05-31

  1. Shaikh AT, Ferland L, Hood-Cree R, Shaffer L, McNabb SJ. Disruptive Innovation Can Prevent the Next Pandemic. Frontiers in public health 2015; 3
  2. Buckeridge DL. Outbreak detection through automated surveillance: a review of the determinants of detection. J Biomed Inform 2007; 40 (4): 370-379. DOI:10.1016/j.jbi.2006.09.003
  3. Fineberg HV. Pandemic preparedness and response—lessons from the H1N1 influenza of 2009. N Engl J Med 2014; 370 (14): 1335-1342. DOI:10.1056/NEJMra1208802
  4. Veenema T, Tõke J. Early detection and surveillance for biopreparedness and emerging infectious diseases. Online journal of issues in nursing 2006; 11(1)
  5. Morse SS. Public health surveillance and infectious disease detection. Biosecurity and bioterrorism: biodefense strategy, practice, and science 2012; 10 (1): 6-16. DOI:10.1089/bsp.2011.0088
  6. Moon S, Leigh J, Woskie L, Checchi F, Dzau V, Fallah M, Fitzgerald G, Garrett L, Gostin L, Heymann DL. Post-Ebola reforms: ample analysis, inadequate action. Bmj 2017; 356: j280. DOI:10.1136/bmj.j280
  7. Clemmons NS, Gastanaduy PA, Fiebelkorn AP, Redd SB, Wallace GS, Control CfD, Prevention. Measles—United States, January 4-April 2, 2015. MMWR Morb Mortal Wkly Rep 2015; 64 (14): 373-376.
  8. Gerbier-Colomban S, Potinet-Pagliaroli V, Metzger M-H. Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic? BMC infectious diseases 2014; 14 (1): 381. DOI:10.1186/1471-2334-14-381
  9. Control CfD, Prevention. State electronic disease surveillance systems---United States, 2007 and 2010. MMWR: Morbidity and mortality weekly report 2011; 60 (41): 1421-1423.
  10. Dixon BE, Siegel JA, Oemig TV, Grannis SJ. Towards Interoperability for public health surveillance: experiences from two states. Online journal of public health informatics 2013; 5(1)
  11. Gesteland PH, Wagner MM, Chapman WW, Espino JU, Tsui F-C, Gardner RM, Rolfs RT, Dato V, James BC, Haug PJ. Rapid deployment of an electronic disease surveillance system in the state of Utah for the 2002 Olympic winter games. Proc AMIA Symp 2002: 285-289
  12. Centers for Disease Control and Prevention, National Syndromic Surveillance Program (NSSP) - BioSense Platform 2003 [updated March 31, 2016 accessed Apr 2016]. Available from: http://www.cdc.gov/nssp/biosense/index.html
  13. Lombardo J, Burkom H, Elbert E, Magruder S, Lewis SH, Loschen W, Sari J, Sniegoski C, Wojcik R, Pavlin J. A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II). J Urban Health 2013; 80 (1): i32-i42.
  14. Ferraro J, Haug P, Mynam K, Post H, Li Y, Jephson A, Stoddard G, Vines C, Allen T, Dean N. Performance of a real-time electronic screening tool for pneumonia. Am J Respir Crit Care Med 2012; 185: A5136.
  15. Dean NC, Jones BE, Ferraro JP, Vines CG, Haug PJ. Performance and utilization of an emergency department electronic screening tool for pneumonia. JAMA Intern Med 2013; 173 (8): 699-701. DOI:10.1001/jamainternmed.2013.3299
  16. Moore CR, Farrag A, Ashkin E. Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports. J Patient Saf 2014
  17. Ye Y, Tsui F, Wagner M, Espino JU, Li Q. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. J Am Med Inform Assoc 2014; 21 (5): 815-823. DOI:10.1136/amiajnl-2013-001934
  18. Liao KP, Cai T, Savova GK, Murphy SN, Karlson EW, Ananthakrishnan AN, Gainer VS, Shaw SY, Xia Z, Szolovits P. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. bmj 2015; 350: h1885. DOI:10.1136/bmj.h1885
  19. Castro VM, Minnier J, Murphy SN, Kohane I, Churchill SE, Gainer V, Cai T, Hoffnagle AG, Dai Y, Block S. Validation of electronic health record phenotyping of bipolar disorder cases and controls. American Journal of Psychiatry 2015; 172 (4): 363-372. DOI:10.1176/appi.ajp.2014.14030423
  20. Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. The Oxford University Press; 2013
  21. Chapman WW, Dowling JN, Ivanov O, Gesteland PH, Olszewski R, Espino JU, Wagner MM, editors. Evaluating natural language processing applications applied to outbreak and disease surveillance. Proceedings of 36th symposium on the interface: computing science and statistics. 2004; Citeseer
  22. Chapman WW, Gundlapalli AV, South BR, Dowling JN. Natural language processing for biosurveillance. In: Castillo-Chavez C, Chen H, Lober WB, Thurmond M, Zeng D, editors. Infectious Disease Informatics and Biosurveillance: Springer; 2011. p. 279-310
  23. Dublin S, Baldwin E, Walker RL, Christensen LM, Haug PJ, Jackson ML, Nelson JC, Ferraro J, Carrell D, Chapman WW. Natural Language Processing to identify pneumonia from radiology reports. Pharmacoepidemiol Drug Saf 2013; 22 (8): 834-841. DOI:10.1002/pds.3418
  24. Gundlapalli AV, Carter ME, Palmer M, Ginter T, Redd A, Pickard S, Shen S, South B, Divita G, Duvall S. Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans. AMIA Annu Symp Proc 2013; Nov 16 2013: 537-546
  25. Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support? J Biomed Inform 2009; 42 (5): 760-772. DOI:10.1016/j.jbi.2009.08.007
  26. Elkin PL, Froehling DA, Wahner-Roedler DL, Brown SH, Bailey KR. Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes. Annals of Internal Medicine 2012; 156 (1_Part_1): 11-18. DOI:10.7326/0003-4819-156-1-201201030-00003
  27. Lippincott T, Séaghdha DÓ, Korhonen A. Exploring subdomain variation in biomedical language. BMC Bioinformatics 2011; 12 (1): 1. DOI:10.1186/1471-2105-12-212
  28. Chapman WW, Nadkarni PM, Hirschman L, D‘Avolio LW, Savova GK, Uzuner O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc 2011; 18 (5): 540-543. DOI:10.1136/amiajnl-2011-000465
  29. Daumé III H. Frustratingly easy domain adaptation. Proc 45th Ann Meeting of the Assoc Compuational Linguistics 2007; 45 (1): 256-263.
  30. Dredze M, Blitzer J, Talukdar PP, Ganchev K, Graca J, Pereira FC. Frustratingly Hard Domain Adaptation for Dependency Parsing. Conference on Empirical Methods in Natural Language Processing 2007: 1051-1055
  31. Ferraro JP, Daumé H, DuVall SL, Chapman WW, Harkema H, Haug PJ. Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation. J Am Med Inform Assoc 2013; 20 (5): 931-939. DOI:10.1136/amiajnl-2012-001453
  32. Teixeira PL, Wei W-Q, Cronin RM, Mo H, VanHouten JP, Carroll RJ, LaRose E, Bastarache LA, Rosenbloom ST, Edwards TL. Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals. Journal of the American Medical Informatics Association 2016: ocw071
  33. Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, Zink RM, Pacheco JA, Boomershine CS, Lasko TA, Xu H. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. Journal of the American Medical Informatics Association 2012; 19 (e1): e162-e169. DOI:10.1136/amiajnl-2011-000583
  34. Tsui F, Wagner M, Cooper G, Que J, Harkema H, Dowling J, Sriburadej T, Li Q, Espino J, Voorhees R. Probabilistic case detection for disease surveillance using data in electronic medical records. Online J Public Health Inform 2011; 3(3)
  35. Russell S, Norvig P. Artificial Intelligence: A Modern Approach. Prentice Hall; 2009. p. 272-319
  36. Bodenreider O. The Unified Medical Language Yystem (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004; 32 (suppl 1): D267-D270. DOI:10.1093/nar/gkh061
  37. Samore MH. Natutal language processing: Can it help detect cases and characterize outbreaks? Advances in Disease Surveillance 2008; 5(59)
  38. Pineda AL, Tsui F-C, Visweswaran S, Cooper GF. Detection of patients with influenza syndrome using machine-learning models learned from emergency department reports. Online J Public Health Inform 2013; 5(1)
  39. Mehrabi S, Wang Y, Ihrke D, Liu H. Exploring Gaps of Family History Documentation in EHR for Precision Medicine-A Case Study of Familial Hypercholesterolemia Ascertainment. AMIA Summits on Translational Science Proceedings 2016; 2016: 160.
  40. Sohn S, Wi C-i, Krusemark EA, Liu H, Ryu E, Wu S, Juhn YJ. Assessment of Asthma Progression Determined by Natural Language Processing to Improve Asthma Care and Research in the Era of Electronic Medical Records. The Journal of Allergy and Clinical Immunology 2017; 139 (2): AB100. DOI:10.1016/j.jaci.2016.12.327
  41. Liu H, Bielinski SJ, Sohn S, Murphy S, Kavishwar BW, Jonnalagadda SR, Ravikumar KE, Wu ST, Kullo IJ, Chute CG. An information extraction framework for cohort identification using electronic health records. AMIA Jt Summits Transl Sci Proc 2013: 149-153
  42. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association 2010; 17 (5): 507-513. DOI:10.1136/jamia.2009.001560
  43. Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering 2004; 10 (3-4): 327-348. DOI:10.1017/S1351324904003523
  44. Denny JC, Spickard A, Johnson KB, Peterson NB, Peterson JF, Miller RA. Evaluation of a method to identify and categorize section headers in clinical documents. Journal of the American Medical Informatics Association 2009; 16 (6): 806-815. DOI:10.1197/jamia.M3037
  45. Darwiche A. Modeling and reasoning with Bayesian networks: Cambridge University Press; 2009
  46. Ferraro JP, Allen TL, Briggs B, Haug P, Post H, editors. Development and function of a real-time web-based screening system for emergency department patients with occult septic shock. 2008 Annual Meeting - Socity for Academic Emergency Medicine; 2008; Washington, DC
  47. J Leng, S Shen, A Gundlapalli, South B, editors. The Extensible Human Oracle Suite of Tools (eHOST) for Annotation of Clinical Narratives. AMIA Spring Congress; 2010; Phoenix, AZ
  48. Fleiss JL. Measuring nominal scale agreement among many raters. Psychol Bull 1971; 76 (5): 378. DOI:10.1037/h0031619
  49. Cooper G, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine learning. 1992; 9 (4): 309-347.
  50. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995: 289-300
  51. Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical Recipes in C: The Art of Scientific Computing. 3rd ed. New York, NY: Cambridge University Press; 2007
  52. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988: 837-845
  53. Aronson AR, Lang F-M. An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association 2010; 17 (3): 229-236. DOI:10.1136/jamia.2009.002733
  54. Patterson O, Hurdle JF, editors. Document clustering of clinical narratives: a systematic study of clinical sublanguages. AMIA Annu Symp Proc; 2011; Citeseer
  55. Ferraro JP, Daumé H, DuVall SL, Chapman WW, Harkema H, Haug PJ. Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation. Journal of the American Medical Informatics Association 2013; 20 (5): 931-939. DOI:10.1136/amiajnl-2012-001453
  56. Cooper GF, Villamarin R, Tsui F-CR, Millett N, Espino JU, Wagner MM. A method for detecting and characterizing outbreaks of infectious disease from clinical reports. Journal of biomedical informatics 2015; 53: 15-26. DOI:10.1016/j.jbi.2014.08.011
  57. Pineda AL, Ye Y, Visweswaran S, Cooper GF, Wagner MM, Tsui FR. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. Journal of Biomedical Informatics 2015; 58: 60-69. DOI:10.1016/j.jbi.2015.08.019
  58. Shi Y, Sha F. Information-theoretical learning of discriminative clusters for unsupervised domain adaptation. Proceedings of International Conference on Machine Learning 2012: 1079-1086
  59. Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW. A theory of learning from different domains. Mach Learn 2010; 79 (1-2): 151-175. DOI:10.1007/s10994-009-5152-4
  60. Blitzer J, Kakade S, Foster DP, editors. Domain adaptation with coupled subspaces. International Conference on Artificial Intelligence and Statistics; 2011

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