Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning

  • Feifan LiuEmail author
  • Chunhua Weng
  • Hong Yu
Part of the Health Informatics book series (HI)


Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and explains how traditional machine learning and the state-of-the-art deep learning techniques can be employed in the context of extracting and transforming narrative information in EHR to support clinical research.


Electronic health records Biomedical natural language processing Rule-based approach Machine learning Deep learning Clinical research 


  1. 1.
    Sox HC, Greenfield S. Comparative effectiveness research: a report from the Institute of Medicine. Ann Intern Med. 2009;151:203–5.PubMedCrossRefGoogle Scholar
  2. 2.
    NIH VideoCasting Event Summary. Accessed 18 May 2011.
  3. 3.
    Clinical Research & Clinical Trials. Accessed 17 May 2011.
  4. 4.
    Sung NS, Crowley WF, Genel M, Salber P, Sandy L, Sherwood LM, et al. Central challenges facing the national clinical research enterprise. JAMA. 2003;289:1278–87.CrossRefGoogle Scholar
  5. 5.
    Most physicians do not participate in clinical trials because of lack of opportunity, time, personnel support and resources. Accessed 31 Aug 2010.
  6. 6.
    Clinical and Translational Science Awards. 2007. Accessed 31 Aug 2010.
  7. 7.
    Garets D, Davis M. Electronic medical records vs. electronic health records: yes, there is a difference. A HIMSS analytics white paper Chicago: HIMSS Analytics. 2005.Google Scholar
  8. 8.
    Garets D, Davis M. Electronic patient records, EMRs and EHRs: concepts as different as apples and oranges at least deserve separate names. Healthcare Informatics online. 2005;22:53–54.Google Scholar
  9. 9.
    File:VistA Img.png – wikipedia, the free encyclopedia. Accessed 18 Aug 2010.
  10. 10.
    Walker EP. More doctors are using electronic medical records. 2010. Accessed 18 Aug 2010.
  11. 11.
    Population Estimates. Accessed 17 May 2011.
  12. 12.
    Hazlehurst B, Mullooly J, Naleway A, Crane B. Detecting possible vaccination reactions in clinical notes. In:AMIA annual symposium proceedings; 2005. p. 306–10.Google Scholar
  13. 13.
    Pakhomov S, Weston SA, Jacobsen SJ, Chute CG, Meverden R, Roger VL. Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care. 2007;13(6 Part 1):281–8.PubMedGoogle Scholar
  14. 14.
    Liao KP, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, Raychaudhuri S, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken). 2010;62:1120–7.CrossRefGoogle Scholar
  15. 15.
    Brownstein JS, Murphy SN, Goldfine AB, Grant RW, Sordo M, Gainer V, et al. Rapid identification of myocardial infarction risk associated with diabetes medications using electronic medical records. Diabetes Care. 2010;33:526–31.PubMedCrossRefGoogle Scholar
  16. 16.
    Reis BY, Kohane IS, Mandl KD. Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study. BMJ. 2009;339:b3677.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc. 2009;16:328–37.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Chute CG. The horizontal and vertical nature of patient phenotype retrieval: new directions for clinical text processing. Proc AMIA Symp. 2002:165–9.Google Scholar
  19. 19.
    Thadani SR, Weng C, Bigger JT, Ennever JF, Wajngurt D. Electronic screening improves efficiency in clinical trial recruitment. J Am Med Inform Assoc. 2009;16:869–73.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Embi PJ, Payne PRO. Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc. 2009;16:316–27.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Kuehn BM. Institute of Medicine outlines priorities for comparative effectiveness research. JAMA. 2009;302:936–7.PubMedCrossRefGoogle Scholar
  22. 22.
    Grishman R, Hirschman L, Nhan NT. Discovery procedures for sublanguage selectional patterns: initial experiments. Comput Linguist. 1986;12:205–15.Google Scholar
  23. 23.
    Harris Z. Mathematical Structures of Language. New York and London: Interscience Publishers; 1968.Google Scholar
  24. 24.
    Grishman R, Kittredge R. Analyzing language in restricted domains: sublanguage description and processing. Hillsdale, N.J: Lawrence Erlbaum Associates; 1986.Google Scholar
  25. 25.
    Johnson SB, Gottfried M. Sublanguage analysis as a basis for a controlled medical vocabulary. In:Proceedings symposium on computer applications in medical care; 1989. p. 519–23.Google Scholar
  26. 26.
    Bronzino JD. The biomedical engineering handbook. Florida: Springer; 2000.Google Scholar
  27. 27.
    Friedman C, Kra P, Rzhetsky A. Two biomedical sublanguages: a description based on the theories of Zellig Harris. J Biomed Inform. 2002;35:222–35.PubMedCrossRefGoogle Scholar
  28. 28.
    Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction: New York: Springer; 2001.CrossRefGoogle Scholar
  29. 29.
    Bishop C. Pattern recognition and machine learning (Information Science and Statistics): Springer; 2007. Accessed 15 Jul 2010.
  30. 30.
    Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference: Morgan Kaufmann Publishers; 1988. Accessed 12 Jul 2010.
  31. 31.
    Michalski RS, Carbonell JG, Mitchell TM. Machine learning: an artificial intelligence approach. Berlin Heidelberg: Springer-Verlag; 1983.Google Scholar
  32. 32.
    Manning CD, Schütze H. Foundations of statistical natural language processing. Cambridge, MA: MIT Press; 2000.Google Scholar
  33. 33.
    Bayes M, Price M. An essay towards solving a problem in the doctrine of chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S. Philos Trans (1683–1775). 1763;53:370–418.CrossRefGoogle Scholar
  34. 34.
    Pearl J. Bayesian networks: a model of self-activated memory for evidential reasoning. In: Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine. 1985. p. 334, 329. Accessed 15 Jul 2010.
  35. 35.
    Verduijn M, Peek N, Rosseel PMJ, de Jonge E, De Mol BAJM. Prognostic Bayesian networks: I: rationale, learning procedure, and clinical use. J Biomed Inform. 2007;40:609–18.PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol. 2000;7:601–20.PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Baum LE, Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Annals Math Stat. 1966;37:1554–63.CrossRefGoogle Scholar
  38. 38.
    Lukashin AV, Borodovsky M. GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 1998;26:1107–15.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Yu L, Smith TF. Positional statistical significance in sequence alignment. J Comput Biol. 1999;6:253–9.PubMedCrossRefGoogle Scholar
  40. 40.
    Kindermann R. Markov random fields and their applications (Contemporary Mathematics; V. 1). American Mathematical Society. Accessed 16 Jul 2010.
  41. 41.
    Komodakis N, Besbes A, Glocker B, Paragios N. Biomedical image analysis using Markov random fields & efficient linear programing. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6628–31.PubMedGoogle Scholar
  42. 42.
    Lee N, Laine AF, Smith RT. Bayesian transductive Markov random fields for interactive segmentation in retinal disorders. In: World congress on medical physics and biomedical engineering, September 7–12, 2009, Munich. 2009. 227–30. Accessed 16 Jul 2010.Google Scholar
  43. 43.
    Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.Google Scholar
  44. 44.
    Pavlopoulos S, Stasis A, Loukis E. A decision tree – based method for the differential diagnosis of aortic stenosis from mitral regurgitation using heart sounds. Biomed Eng Online. 2004;3:21.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Suresh A, Karthikraja V, Lulu S, Kangueane U, Kangueane P. A decision tree model for the prediction of homodimer folding mechanism. Bioinformation. 2009;4:197–205.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Pearl R, Reed LJ. A further note on the mathematical theory of population growth. Proc Natl Acad Sci USA. 1922;8:365–8.PubMedCrossRefGoogle Scholar
  47. 47.
    Bagley SC, White H, Golomb BA. Logistic regression in the medical literature:: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. 2001;54:979–85.PubMedCrossRefGoogle Scholar
  48. 48.
    Gareen IF, Gatsonis C. Primer on multiple regression models for diagnostic imaging research. Radiology. 2003;229:305–10.PubMedCrossRefGoogle Scholar
  49. 49.
    Vapnik VN. The nature of statistical learning theory. New York: Springer-Verlag; 1995. Accessed 19 Jul 2010CrossRefGoogle Scholar
  50. 50.
    Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA. 2000;97:262–7.PubMedCrossRefGoogle Scholar
  51. 51.
    Polavarapu N, Navathe SB, Ramnarayanan R, Ul Haque A, Sahay S, Liu Y. Investigation into biomedical literature classification using support vector machines. In:Proceedings IEEE computational systems bioinformatics conference; 2005. p. 366–74.Google Scholar
  52. 52.
    Takeuchi K, Collier N. Bio-medical entity extraction using Support Vector Machines. In:Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine – Volume 13. Sapporo: Association for Computational Linguistics; 2003. p. 57–64. Accessed 19 Jul 2010.CrossRefGoogle Scholar
  53. 53.
    Pan C, Yan X, Zheng C. Hard Margin SVM for biomedical image segmentation. In:Advances in neural networks – ISNN 2005; 2005. p. 754–9. Accessed 19 Jul 2010.CrossRefGoogle Scholar
  54. 54.
    Fix E, Hodges JL. Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review / Revue Internationale de Statistique. 1989;57:238–47.Google Scholar
  55. 55.
    Pan F, Wang B, Hu X, Perrizo W. Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis. J Biomed Inform. 2004;37:240–8.PubMedCrossRefGoogle Scholar
  56. 56.
    Shanmugasundaram V, Maggiora GM, Lajiness MS. Hit-directed nearest-neighbor searching. J Med Chem. 2005;48:240–8.PubMedCrossRefGoogle Scholar
  57. 57.
    Qi Y, Klein-Seetharaman J, Bar-Joseph Z. Random forest similarity for protein-protein interaction prediction from multiple sources. In:Pacific symposium on biocomputing; 2005. p. 531–42.Google Scholar
  58. 58.
    Barbini P, Cevenini G, Massai MR. Nearest-neighbor analysis of spatial point patterns: application to biomedical image interpretation. Comput Biomed Res. 1996;29:482–93.PubMedCrossRefGoogle Scholar
  59. 59.
    McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol. 1990;52:99–115.PubMedCrossRefGoogle Scholar
  60. 60.
    Xue Q, Reddy BRS. Late potential recognition by artificial neural networks. Biomed Eng, IEEE Trans on. 1997;44:132–43.CrossRefGoogle Scholar
  61. 61.
    Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–9.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Jerez-Aragonés JM, Gómez-Ruiz JA, Ramos-Jiménez G, Muñoz-Pérez J, Alba-Conejo E. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med. 2003;27:45–63.PubMedCrossRefGoogle Scholar
  63. 63.
    Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN, FEH J, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79:857–62.PubMedCrossRefGoogle Scholar
  64. 64.
    Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In:Machine learning-international workshop then conference; 2001. p. 282–9.Google Scholar
  65. 65.
    McCallum A, Freitag D, Pereira FCN. Maximum Entropy Markov models for information extraction and segmentation. In:Proceedings of the seventeenth international conference on machine learning: Morgan Kaufmann Publishers; 2000. p. 591–8. Accessed 20 Jul 2010.
  66. 66.
    Settles B. ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics. 2005;21:3191–2.PubMedCrossRefGoogle Scholar
  67. 67.
    Leaman R, Gonzalez G. Banner: an executable survey of advances in biomedical named entity recognition. In: Pacific Symposium on Biocomputing. 2008. p. 652–663.Google Scholar
  68. 68.
    Bundschus M, Dejori M, Stetter M, Tresp V, Kriegel H-P. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinforma. 2008;9:207.CrossRefGoogle Scholar
  69. 69.
    Sarafraz F, Eales J, Mohammadi R, Dickerson J, Robertson D, Nenadic G. Biomedical event detection using rules, conditional random fields and parse tree distances. In:Proceedings of the workshop on BioNLP: shared task. Boulder: Association for Computational Linguistics; 2009. p. 115–8. Accessed 21 Jul 2010.CrossRefGoogle Scholar
  70. 70.
    Forgy E. Cluster analysis of multivariate data: efficiency vs. interpretability of classifications. Biometrics. 1965;21:768.Google Scholar
  71. 71.
    Jardine N, Sibson R. Mathematical taxonomy. Wiley; 1971.Google Scholar
  72. 72.
    McLachlan GJ, Basford KE. Mixture models: inference and applications to clustering. New York, N.Y.: Marcel Dekker; 1988.Google Scholar
  73. 73.
    Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA. 1999;96:2907.PubMedCrossRefGoogle Scholar
  74. 74.
    De Smet F, Mathys J, Marchal K, Thijs G, De Moor B, Moreau Y. Adaptive quality-based clustering of gene expression profiles. Bioinformatics. 2002;18:735.PubMedCrossRefGoogle Scholar
  75. 75.
    Sheng Q, Moreau Y, De Moor B. Biclustering microarray data by Gibbs sampling. Bioinformatics. 2003;19(2):196–205.Google Scholar
  76. 76.
    Schafer J, Strimmer K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics. 2005;21:754.PubMedCrossRefGoogle Scholar
  77. 77.
    Deng L, Yu D. Deep learning: methods and applications. Found Trends Signal Process. 2014;7:197–387.CrossRefGoogle Scholar
  78. 78.
    Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. arXiv:170802709 [cs]. 2017. Accessed 6 Jul 2018.
  79. 79.
    Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering. In:Proceedings of the 24th international conference on machine learning. New York: ACM; 2007. p. 791–8. Scholar
  80. 80.
    Hinton G, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18:1527–54.PubMedCrossRefGoogle Scholar
  81. 81.
    Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res. 2010;11:3371–408.Google Scholar
  82. 82.
    Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012. p. 1097–1105. Accessed 9 Feb 2015.
  83. 83.
    Socher R, Lin CC, Manning C, Ng AY. Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11). 2011. p. 129–136. Accessed 9 Feb 2015.
  84. 84.
    Iftene M, Liu Q, Wang Y. Very high resolution images classification by fine tuning deep convolutional neural networks. In: Eighth International Conference on Digital Image Processing (ICDIP 2016). International Society for Optics and Photonics; 2016. p. 100332D.
  85. 85.
    Sager N, Friedman C, Chi E. The analysis and processing of clinical narrative. Fortschr Med. 1986;86:1101–5.Google Scholar
  86. 86.
    Sager N, Friedman C, Lyman MS. Medical language processing: computer management of narrative data. First Edition. Addison-Wesley; 1987.Google Scholar
  87. 87.
    Friedman C, Alderson PO, Austin JH, Cimino JJ, Johnson SB. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc. 1994;1:161–74.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Gold S, Elhadad N, Zhu X, Cimino JJ, Hripcsak G. Extracting structured medication event information from discharge summaries. AMIA Annu Symp Proc. 2008;2008:237–41.PubMedCentralPubMedGoogle Scholar
  89. 89.
    Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc. 2010;17:19–24.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Haug PJ, Koehler S, Lau LM, Wang P, Rocha R, Huff SM. Experience with a mixed semantic/syntactic parser. In:Proceedings of the annual symposium on computer application in medical care; 1995. p. 284–8.Google Scholar
  91. 91.
    Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc. 2000;7:593–604.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Agarwal S, Yu H. Biomedical negation scope detection with conditional random fields. J Am Med Inform Assoc. 2010;17:696–701.PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Agarwal S, Yu H. Detecting hedge cues and their scope in biomedical literature with conditional random fields. J Biomed Inform. 2010;43(6):953–61. Scholar
  94. 94.
    Vincze V, Szarvas G, Farkas R, Mora G, Csirik J. The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinforma. 2008;9(11):S9.CrossRefGoogle Scholar
  95. 95.
    Li Z, Liu F, Antieau L, Cao Y, Yu H. Lancet: a high precision medication event extraction system for clinical text. J Am Med Inform Assoc. 2010;17:563–7.PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Rennie J. Boosting with decision stumps and binary features. Relation. 2003;10 1.33:1666.Google Scholar
  97. 97.
    Cao Y, Liu F, Simpson P, Antieau L, Bennett A, Cimino JJ, et al. AskHERMES: an online question answering system for complex clinical questions. J Biomed Inform. 2011;44:277–88.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Cao Y, Cimino JJ, Ely J, Yu H. Automatically extracting information needs from complex clinical questions. J Biomed Inform. In Press, Uncorrected Proof. Scholar
  99. 99.
    Liu F, Tur G, Hakkani-Tür D, Yu H. Towards spoken clinical question answering: evaluating and adapting automatic speech recognition systems for spoken clinical questions. J Am Med Inform Assoc. 2011;18:625–30.PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Stolcke A, Anguera X, Boakye K, Çetin Ö, A Janin Mandal A, et al. Further progress in meeting recognition: the ICSI-SRI spring 2005 speech-to-text evaluation system. 3869, LNCS, MLMI Workshop. 2005;78:463–75.Google Scholar
  101. 101.
    Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, et al. Clinical information extraction applications: a literature review. J Biomed Inform. 2018;77:34–49.PubMedCrossRefGoogle Scholar
  102. 102.
    Roberts K, Rink B, Harabagiu SM, Scheuermann RH, Toomay S, Browning T, et al. A machine learning approach for identifying anatomical locations of actionable findings in radiology reports. AMIA Annu Symp Proc. 2012;2012:779–88.PubMedPubMedCentralGoogle Scholar
  103. 103.
    Li Q, Spooner SA, Kaiser M, Lingren N, Robbins J, Lingren T, et al. An end-to-end hybrid algorithm for automated medication discrepancy detection. BMC Med Inform Decis Mak. 2015;15
  104. 104.
    Sarker A, Gonzalez G. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J Biomed Inform. 2015;53:196–207.PubMedCrossRefGoogle Scholar
  105. 105.
    Rochefort CM, Buckeridge DL, Forster AJ. Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol. Implement Sci. 2015;10:5.PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Yadav K, Sarioglu E, Smith M, Choi H-A. Automated outcome classification of emergency department computed tomography imaging reports. Acad Emerg Med. 2013;20:848–54.PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Barrett N, Weber-Jahnke JH, Thai V. Engineering natural language processing solutions for structured information from clinical text: extracting sentinel events from palliative care consult letters. Stud Health Technol Inform. 2013;192:594–8.PubMedGoogle Scholar
  108. 108.
    Tang B, Cao H, Wang X, Chen Q, Xu H. Evaluating word representation features in biomedical named entity recognition tasks. Biomed Res Int. 2014;2014(240403):1–6.Google Scholar
  109. 109.
    Liu S, Tang B, Chen Q, Wang X. Effects of semantic features on machine learning-based drug name recognition systems: word embeddings vs. manually constructed dictionaries. Information. 2015;6:848–65.CrossRefGoogle Scholar
  110. 110.
    De Vine L, Zuccon G, Koopman B, Sitbon L, Bruza P. Medical semantic similarity with a neural language model. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM; 2014. p. 1819–1822. Accessed 4 Jun 2016.
  111. 111.
    Wu Y, Xu J, Zhang Y, Xu H. Clinical abbreviation disambiguation using neural word embeddings. In:Proceedings of the 2015 workshop on biomedical natural language processing; 2015. p. 171–6.Google Scholar
  112. 112.
    Liu Y, Ge T, Mathews KS, Ji H, McGuinness DL. Exploiting task-oriented resources to learn word embeddings for clinical abbreviation expansion. In:Proceedings of the 2015 workshop on biomedical natural language processing; 2015. p. 92–7.Google Scholar
  113. 113.
    Henriksson A, Kvist M, Dalianis H, Duneld M. Identifying adverse drug event information in clinical notes with distributional semantic representations of context. J Biomed Inform. 2015;57:333–49.PubMedCrossRefGoogle Scholar
  114. 114.
    Ghassemi MM, Mark RG, Nemati S. A visualization of evolving clinical sentiment using vector representations of clinical notes. In: 2015 Computing in cardiology conference (CinC). 2015. p. 629–32.Google Scholar
  115. 115.
    Choi E, Bahadori MT, Searles E, Coffey C, Sun J. Multi-layer representation learning for medical concepts. In: Proceedings of 22nd ACM SIGKDD conference on knowledge discovery and data mining. 2016. Accessed 10 Mar 2016.
  116. 116.
    Choi Y, Chiu CY-I, Sontag D. Learning low-dimensional representations of medical concepts. AMIA Jt Summits Transl Sci Proc. 2016;2016:41–50.PubMedPubMedCentralGoogle Scholar
  117. 117.
    Jagannatha A, Yu H. Bidirectional RNN for medical event detection in electronic health records. San Diego; 2016. p. 473–82.
  118. 118.
    Jagannatha A, Yu H. Structured prediction models for RNN based sequence labeling in clinical text. 2016. Accessed 28 Aug 2016.
  119. 119.
    Munkhdalai T, Liu F, Yu H. Clinical relation extraction toward drug safety surveillance using electronic health record narratives: classical learning versus deep learning. JMIR Public Health Surveill. 2018;4:e29.PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    Li R, Yu H. A hybrid neural network model for joint prediction of medical presence and period assertions in clinical notes. In: AMIA fall symposium. 2017.Google Scholar
  121. 121.
    Choi E, Bahadori MT, Sun J. Doctor AI. Predicting clinical events via recurrent neural networks. arXiv:151105942 [cs]. 2015. Accessed 9 Mar 2016.
  122. 122.
    Cho K, van Merrienboer B, Gulcehre C, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078. 2014.Google Scholar
  123. 123.
    Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;6:26094. Scholar
  124. 124.
    Shin H-C, Lu L, Kim L, Seff A, Yao J, Summers RM. Interleaved text/image deep mining on a large-scale radiology database. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). 2015. p. 1090–9.Google Scholar
  125. 125.
    Wang X, Peng Y, Lu L, Lu Z, Summers RM. Tienet: text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 9049–58.Google Scholar
  126. 126.
    Gupta D, Saul M, Gilbertson J. Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. Am J Clin Pathol. 2004;121:176–86.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing 2019

Authors and Affiliations

  1. 1.Department of Quantitative Health Sciences and Department of Radiology (Joint)University of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Department of Biomedical InformaticsColumbia UniversityNew YorkUSA
  3. 3.Department of Computer ScienceUniversity of Massachusetts LowellLowellUSA
  4. 4.Bedford VA Medical CenterBedfordUSA
  5. 5.Department of MedicineUniversity of Massachusetts Medical School (Adjunct)WorcesterUSA
  6. 6.College of Information and Computer SciencesUniversity of Massachusetts Amherst (Adjunct)AmherstUSA

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