Skip to main content

Advertisement

Springer Nature Link
Log in

Information extraction from electronic medical documents: state of the art and future research directions

  • Review
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Abacha AB, Zweigenbaum P (2011) Medical entity recognition: a comparaison of semantic and statistical methods. In: Proceedings of BioNLP 2011 workshop, pp 56–64

  2. Aich S, Sain M, Park J, Choi KW, Kim HC (2017) A text mining approach to identify the relationship between gait-parkinson’s disease (pd) from pd based research articles. In: 2017 international conference on inventive computing and informatics (ICICI), IEEE, pp 481–485

  3. Akbik A, Bergmann T, Blythe D, Rasul K, Schweter S, Vollgraf R (2019) Flair: an easy-to-use framework for state-of-the-art nlp. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics (Demonstrations), pp 54–59

  4. Al-Dafas M, Albujeer A, Hussien SA, Ibrahim RK (2022) On the adaption of data mining technology to categorize cancer diseases. Int J Artif Intell Inform 3(2):80–91

    Google Scholar 

  5. Alex B, Grover C, Tobin R, Sudlow C, Mair G, Whiteley W (2019) Text mining brain imaging reports. J Biomed Semant 10(1):1–11

    Google Scholar 

  6. Angeli G, Premkumar MJJ, Manning CD (2015) Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing vol 1: Long Papers, pp 344–354

  7. Apostolova E, Channin DS, Demner-Fushman D, Furst J, Lytinen S, Raicu D (2009) Automatic segmentation of clinical texts. In: 2009 Annual international conference of the IEEE engineering in medicine and biology society, IEEE, pp 5905–5908

  8. Arbabi A, Adams DR, Fidler S, Brudno M (2019) Identifying clinical terms in medical text using ontology-guided machine learning. JMIR Med Inform 7(2):e12,596

    Article  MATH  Google Scholar 

  9. Aronson AR, Lang FM (2010) An overview of metamap: historical perspective and recent advances. J Am Med Inform Assoc 17(3):229–236

    Article  Google Scholar 

  10. Aydar M, Bozal O, Ozbay F (2020) Neural relation extraction: a survey. arXiv e-prints pp arXiv–2007

  11. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP (1987) Dxplain: an evolving diagnostic decision-support system. Jama 258(1):67–74

    Article  Google Scholar 

  12. Batista DS (2018) Named-entity evaluation metrics based on entity-level. http://www.davidsbatista.net/blog/2018/05/09/Named_Entity_Evaluation

  13. Beel J, Gipp B, Shaker A, Friedrich N (2010) Sciplore xtract: extracting titles from scientific pdf documents by analyzing style information (font size). International conference on theory and practice of digital libraries. Springer, Cham, pp 413–416

    Google Scholar 

  14. Ben Abdessalem Karaa W, Alkhammash EH, Bchir A (2021) Drug disease relation extraction from biomedical literature using nlp and machine learning. Mob Inf Syst. https://doi.org/10.1155/2021/9958410

    Article  Google Scholar 

  15. Berrazega I (2012) Temporal information processing: a survey. Int J Nat Lang Comput 1(2):1–14

    Article  Google Scholar 

  16. Bethard S, Savova G, Chen WT, Derczynski L, Pustejovsky J, Verhagen M (2016) Semeval-2016 task 12: clinical tempeval. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 1052–1062

  17. Bethard S, Savova G, Palmer M, Pustejovsky J (2017) SemEval-2017 task 12: clinical tempEval. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017), Association for Computational Linguistics, Vancouver, Canada, pp 565–572. 10.18653/v1/S17-2093

  18. Bhatia P, Celikkaya B, Khalilia M (2019) Joint entity extraction and assertion detection for clinical text. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, vol 1: Long Papers, Association for Computational Linguistics, pp 954–959. 10.18653/v1/p19-1091

  19. Bodenreider O (2004) The unified medical language system (umls): integrating biomedical terminology. Nucleic Acid Res 32(suppl 1):D267–D270

    Article  Google Scholar 

  20. Bottou L (1999) On-line learning and stochastic approximations. Cambridge University Press, USA, pp 9–42

    MATH  Google Scholar 

  21. Bramsen P, Deshpande P, Lee YK, Barzilay R (2006) Finding temporal order in discharge summaries. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2006, p 81

  22. Chapman W, Dowling J, Chu D (2007) Context: an algorithm for identifying contextual features from clinical text. Biological, translational, and clinical language processing. University of Pittsburgh, Pittsburgh, PA, pp 81–88

    Google Scholar 

  23. Chapman WW, Savova GK, Zheng J, Tharp M, Crowley R (2012) Anaphoric reference in clinical reports: characteristics of an annotated corpus. J Biomed Inform 45(3):507–521

    Article  Google Scholar 

  24. Chaves L, Marques G (2021) Data mining techniques for early diagnosis of diabetes: a comparative study. Appl Sci 11(5):2218

    Article  Google Scholar 

  25. Chirila OS, Chirila CB, Stoicu-Tivadar L (2019) Improving the prescription process information support with structured medical prospectuses using neural networks. Stud Health Technol Inform 264:353–357

    Google Scholar 

  26. Chirila OS, Chirila CB, Stoicu-Tivadar L (2019) Named entity recognition and classification for medical prospectuses. Stud Health Technol Inform 262:284–287

    Google Scholar 

  27. Cohen KB, Lanfranchi A, Choi MJY, Bada M, Baumgartner WA, Panteleyeva N, Verspoor K, Palmer M, Hunter LE (2017) Coreference annotation and resolution in the colorado richly annotated full text (craft) corpus of biomedical journal articles. BMC Bioinform 18(1):1–14

    Article  Google Scholar 

  28. Cohen KB, Verspoor K, Fort K, Funk C, Bada M, Palmer M, Hunter LE (2017) The colorado richly annotated full text (craft) corpus: multi-model annotation in the biomedical domain. Handbook of linguistic annotation. Springer, Cham, pp 1379–1394

    Chapter  Google Scholar 

  29. Dai HJ, Syed-Abdul S, Chen CW, Wu CC (2015) Recognition and evaluation of clinical section headings in clinical documents using token-based formulation with conditional random fields. BioMed Res Int. https://doi.org/10.1155/2015/873012

    Article  Google Scholar 

  30. De Bruijn B, Cherry C, Kiritchenko S, Martin J, Zhu X (2011) Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J Am Med Inform Assoc 18(5):557–562

    Article  Google Scholar 

  31. Del Corro L, Gemulla R (2013) Clausie: clause-based open information extraction. In: Proceedings of the 22nd international conference on world wide web, pp 355–366

  32. Deléger L, Névéol A (2014) Automatic identification of document sections for designing a french clinical corpus (identification automatique de zones dans des documents pour la constitution d’un corpus médical en français) [in french]. In: TALN

  33. Deng N, Fu H, Chen X (2021) Named entity recognition of traditional chinese medicine patents based on bilstm-crf. Wirel Commun Mob Comput. https://doi.org/10.1155/2021/6696205

    Article  Google Scholar 

  34. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, Vol 1 (Long and Short Papers), Association for Computational Linguistics, pp 4171–4186. 10.18653/v1/n19-1423

  35. Doğan RI, Leaman R, Lu Z (2014) Ncbi disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inform 47:1–10

    Article  Google Scholar 

  36. Donnelly K (2006) Snomed-ct: the advanced terminology and coding system for ehealth. Stud Health Technol Inform 121:279

    Google Scholar 

  37. Edinger T, Demner-Fushman D, Cohen AM, Bedrick S, Hersh W (2017) Evaluation of clinical text segmentation to facilitate cohort retrieval. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2017, p 660

  38. Elhadad N, Pradhan S, Gorman S, Manandhar S, Chapman W, Savova G (2015) Semeval-2015 task 14: analysis of clinical text. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 303–310

  39. Fader A, Soderland S, Etzioni O (2011) Identifying relations for open information extraction. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp 1535–1545

  40. Fundel K, Küffner R, Zimmer R (2007) Relex-relation extraction using dependency parse trees. Bioinformatics 23(3):365–371

    Article  Google Scholar 

  41. Ghiasvand O, Kate RJ (2018) Learning for clinical named entity recognition without manual annotations. Inform Med Unlocked 13:122–127

    Article  Google Scholar 

  42. Goenaga I, Lahuerta X, Atutxa A, Gojenola K (2021) A section identification tool: towards hl7 cda/ccr standardization in spanish discharge summaries. J Biomed Inform 121(103):875

    Google Scholar 

  43. Grishman R, Sundheim BM (1996) Message understanding conference-6: A brief history. In: Coling 1996 vol 1: the 16th international conference on computational linguistics

  44. Guo F, He R, Dang J (2019) Implicit discourse relation recognition via a bilstm-cnn architecture with dynamic chunk-based max pooling. IEEE Access 7:169,281-169,292

    Article  Google Scholar 

  45. Hafiene N, Karoui W, Romdhane LB (2020) Influential nodes detection in dynamic social networks: a survey. Expert Syst Appl 159(113):642

    Google Scholar 

  46. Hahn U, Oleynik M (2020) Medical information extraction in the age of deep learning. Yearb Med Inform 29(01):208–220

    Article  Google Scholar 

  47. Hallersten A, Fürst W, Mezzasalma R (2016) Physicians prefer greater detail in the biosimilar label (smpc)-results of a survey across seven european countries. Regul Toxicol Pharmacol 77:275–281

    Article  Google Scholar 

  48. Hasan F, Roy A, Pan S (2020) Integrating text embedding with traditional nlp features for clinical relation extraction. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI), IEEE, pp 418–425

  49. Haug PJ, Wu X, Ferraro JP, Savova GK, Huff SM, Chute CG (2014) Developing a section labeler for clinical documents. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2014, p 636

  50. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O (2020) 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. J Am Med Inform Assoc 27(1):3–12

    Article  Google Scholar 

  51. Honnibal M, Montani I (2017) spaCy 2: natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. Appear 7:411–420

    Google Scholar 

  52. Huang M, Liu A, Wang T, Huang C (2018) Green data gathering under delay differentiated services constraint for internet of things. Wirel Commun Mob Comput. https://doi.org/10.1155/2018/9715428

    Article  Google Scholar 

  53. Islamaj R, Leaman R, Kim S, Kwon D, Wei CH, Comeau DC, Peng Y, Cissel D, Coss C, Fisher C, Guzman R, Kochar PG, Koppel S, Trinh D, Sekiya K, Ward J, Whitman D, Schmidt S, Lu Z (2021) Nlm-chem, a new resource for chemical entity recognition in pubmed full text literature. Sci Data 8(1):1–12

    Article  Google Scholar 

  54. Jagannatha A, Liu F, Liu W, Yu H (2019) Overview of the first natural language processing challenge for extracting medication, indication, and adverse drug events from electronic health record notes (made 1.0). Drug Saf 42(1):99–111

    Article  Google Scholar 

  55. Jancsary J, Matiasek J, Trost H (2008) Revealing the structure of medical dictations with conditional random fields. In: Proceedings of the 2008 conference on empirical methods in natural language processing, pp 1–10

  56. Jaouadi M, Romdhane LB (2019) Influence maximization problem in social networks: an overview. In: 2019 IEEE/ACS 16th international conference on computer systems and applications (AICCSA), IEEE, pp 1–8

  57. Jelier R, Jenster G, Dorssers LC, van der Eijk CC, van Mulligen EM, Mons B, Kors JA (2005) Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes. Bioinformatics 21(9):2049–2058

    Article  Google Scholar 

  58. Johnson AE, Pollard TJ, Shen L, Li-Wei HL, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) Mimic-iii, a freely accessible critical care database. Sci Data 3(1):1–9

    Article  Google Scholar 

  59. Karlsson I, Boström H (2016) Predicting adverse drug events using heterogeneous event sequences. In: 2016 IEEE international conference on healthcare informatics (ICHI), IEEE, pp 356–362

  60. Kim Y, Heider PM, Lally IR, Meystre SM (2021) A hybrid model for family history information identification and relation extraction: development and evaluation of an end-to-end information extraction system. JMIR Med Inform 9(4):e22,797

    Article  Google Scholar 

  61. Komariah KS, Shin BK (2021) Medical entity recognition in twitter using conditional random fields. In: 2021 international conference on electronics, information, and communication (ICEIC), IEEE, pp 1–4

  62. Komninos A, Manandhar S (2016) Dependency based embeddings for sentence classification tasks. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 1490–1500

  63. Kouni IBE, Karoui W, Romdhane LB (2021) WLNI-LPA: detecting overlapping communities in attributed networks based on label propagation process. In: Proceedings of the 16th international conference on software technologies, ICSOFT 2021, Online Streaming, July 6 SCITEPRESS, pp 408–416. 10.5220/0010605904080416

  64. Kreuzthaler M, Schulz S (2015) Detection of sentence boundaries and abbreviations in clinical narratives. BMC Med Inform Decis Mak 15:S4–S4

    Article  Google Scholar 

  65. Kroll H, Pirklbauer J, Ruthmann J, Balke W (2020) A semantically enriched dataset based on biomedical NER for the COVID19 open research dataset challenge. CoRR https://arxiv.org/abs/2005.08823

  66. Kropf S, Krücken P, Mueller W, Denecke K (2017) Structuring legacy pathology reports by openehr archetypes to enable semantic querying. Method Inform Med 56(03):230–237

    Article  Google Scholar 

  67. Kumar S (2017) A survey of deep learning methods for relation extraction. arXiv preprint arXiv:1705.03645

  68. Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, ICML ’01, pp 282–289

  69. Lai KH, Topaz M, Goss FR, Zhou L (2015) Automated misspelling detection and correction in clinical free-text records. J Biomed Inform 55:188–195

    Article  Google Scholar 

  70. Lan M, Wang J, Wu Y, Niu ZY, Wang H (2017) Multi-task attention-based neural networks for implicit discourse relationship representation and identification. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 1299–1308

  71. Landolsi MY, Mohamed HH, Romdhane LB (2021) Image annotation in social networks using graph and multimodal deep learning features. Multimed Tool Appl 80(8):12,009-12,034

    Article  Google Scholar 

  72. Laparra E, Xu D, Elsayed A, Bethard S, Palmer M (2018) Semeval 2018 task 6: parsing time normalizations. In: SemEval@ NAACL-HLT, pp 88–96

  73. Laparra E, Su X, Zhao Y, Uzuner O, Miller T, Bethard S (2021) Semeval-2021 task 10: source-free domain adaptation for semantic processing. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 348–356

  74. Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234–1240

    Google Scholar 

  75. Lee W, Choi J (2018) Temporal segmentation for capturing snapshots of patient histories in korean clinical narrative. Healthc Inform Res 24(3):179–186

    Article  Google Scholar 

  76. Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H (2014) A comprehensive study of named entity recognition in chinese clinical text. J Am Med Inform Assoc 21(5):808–814

    Article  Google Scholar 

  77. Leroy G, Chen H (2001) Filling preposition-based templates to capture information from medical abstracts. Biocomputing 2002. World Scientific, Singapore, pp 350–361

    Chapter  Google Scholar 

  78. Li F, Lin Z, Zhang M, Ji D (2021) A span-based model for joint overlapped and discontinuous named entity recognition. CoRR abs/2106.14373, arXiv:2106.14373

  79. Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z (2016) Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database. https://doi.org/10.1093/database/baw068

    Article  Google Scholar 

  80. Li W, Shi S, Gao Z, Wei W, Zhu Q, Lin X, Jiang D, Gao S (2018) Improved deep belief network model and its application in named entity recognition of chinese electronic medical records. In: 2018 IEEE 3rd international conference on big data analysis (ICBDA), IEEE, pp 356–360

  81. Li Y, Lipsky Gorman S, Elhadad N (2010) Section classification in clinical notes using supervised hidden markov model. In: Proceedings of the 1st ACM international health informatics symposium, pp 744–750

  82. Liu F, Li T (2018) A clustering-anonymity privacy-preserving method for wearable iot devices. Secur Commun Netw. https://doi.org/10.1155/2018/4945152

    Article  Google Scholar 

  83. Liu F, Chen J, Jagannatha A, Yu H (2016a) Learning for biomedical information extraction: methodological review of recent advances. CoRR abs/1606.07993, arXiv:1606.07993

  84. Liu Y, Wei L, Yao Z, Fei X (2016) The practice and experience of emergency information system construction. China Dig Med 11(5):53–55

    Google Scholar 

  85. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692, arXiv:1907.11692

  86. Lohr C, Luther S, Matthies F, Hahn U (2018a) Cda-compliant section annotation of german-language discharge summaries: guideline development, annotation campaign, section classification. In: AMIA 2018, american medical informatics association annual symposium, San Francisco, CA, AMIA

  87. Lohr C, Luther S, Matthies F, Modersohn L, Ammon D, Saleh K, Henkel AG, Kiehntopf M, Hahn U (2018b) Cda-compliant section annotation of german-language discharge summaries: guideline development, annotation campaign, section classification. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2018, p 770

  88. Lomotey RK, Deters R (2013) Efficient mobile services consumption in mhealth. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining, pp 982–989

  89. Luan Y, Wadden D, He L, Shah A, Ostendorf M, Hajishirzi H (2019) A general framework for information extraction using dynamic span graphs. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, Vol 1 (Long and Short Papers), Association for Computational Linguistics, Minneapolis, Minnesota, pp 3036–3046. 10.18653/v1/N19-1308

  90. Ludwick DA, Doucette J (2009) Adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries. Int J Med Inform 78(1):22–31

    Article  Google Scholar 

  91. Lupşe O, Stoicu-Tivadar L (2018) Extracting and structuring drug information to improve e-prescription and streamline medical treatment. Appl Med Inform 40(1–2):7–14

    Google Scholar 

  92. Lupşe O, Stoicu-Tivadar L (2018) Supporting prescriptions with synonym matching of section names in prospectuses. Stud Health Technol Inform 251:153–156

    Google Scholar 

  93. Ma F, Liu X, Liu A, Zhao M, Huang C, Wang T (2018) A time and location correlation incentive scheme for deep data gathering in crowdsourcing networks. Wirel Commun Mob Comput. https://doi.org/10.1155/2018/8052620

    Article  Google Scholar 

  94. Mabrouk O, Hlaoua L, Omri MN (2021) Exploiting ontology information in fuzzy svm social media profile classification. Appl Intell 51(6):3757–3774

    Article  Google Scholar 

  95. Mahendran D, McInnes BT (2021) Extracting adverse drug events from clinical notes. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2021, p 420

  96. Manning CD, Surdeanu M, Bauer J, Finkel JR, Bethard S, McClosky D (2014) The stanford corenlp natural language processing toolkit. In: Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–60

  97. Mausam-Schmitz M, Bart R, Soderland S, Etzioni O (2012) Open language learning for information extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Association for Computational Linguistics, USA, p 523-534

  98. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF (2008) Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 17(01):128–144

    Article  Google Scholar 

  99. Mnasri W, Azaouzi M, Romdhane LB (2021) Parallel social behavior-based algorithm for identification of influential users in social network. Appl Intell. https://doi.org/10.1007/s10489-021-02203-x

    Article  MATH  Google Scholar 

  100. Nair N, Narayanan S, Achan P, Soman K (2022) Clinical note section identification using transfer learning. In: Proceedings of sixth international congress on information and communication technology, Springer, pp 533–542

  101. Nasar Z, Jaffry SW, Malik MK (2021) Named entity recognition and relation extraction: state-of-the-art. ACM Comput Surv (CSUR) 54(1):1–39

    Article  Google Scholar 

  102. Nayel HA, ShashrekhaH L (2019) Integrating dictionary feature into a deep learning model for disease named entity recognition. https://arxiv.org/abs/1911.01600

  103. Neumann M, King D, Beltagy I, Ammar W (2019) Scispacy: fast and robust models for biomedical natural language processing. In: Proceedings of the 18th BioNLP workshop and shared task, BioNLP@ACL 2019, Florence, Italy, Association for Computational Linguistics, pp 319–327. 10.18653/v1/w19-5034

  104. Ni J, Delaney B, Florian R (2015) Fast model adaptation for automated section classification in electronic medical records. Stud Health Technol Inform 216:35–39

    Google Scholar 

  105. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, NAACL-HLT, New Orleans, Louisiana, USA, Vol 1 (Long Papers), Association for Computational Linguistics, pp 2227–2237. 10.18653/v1/n18-1202

  106. Pomares-Quimbaya A, Kreuzthaler M, Schulz S (2019) Current approaches to identify sections within clinical narratives from electronic health records: a systematic review. BMC Med Res Methodol 19(1):155

    Article  Google Scholar 

  107. Popovski G, Seljak BK, Eftimov T (2020) A survey of named-entity recognition methods for food information extraction. IEEE Access 8:31,586-31,594

    Article  Google Scholar 

  108. Pradhan S, Elhadad N, Chapman WW, Manandhar S, Savova G (2014) Semeval-2014 task 7: analysis of clinical text. In: SemEval@ COLING, pp 54–62

  109. Qi P, Zhang Y, Zhang Y, Bolton J, Manning CD (2020) Stanza: a python natural language processing toolkit for many human languages. In: Proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations, Association for Computational Linguistics, pp 101–108. 10.18653/v1/2020.acl-demos.14

  110. Quimbaya AP, Múnera AS, Rivera RAG, Rodríguez JCD, Velandia OMM, Peña AAG, Labbé C (2016) Named entity recognition over electronic health records through a combined dictionary-based approach. Procedia Comput Sci 100:55–61

    Article  Google Scholar 

  111. Rebholz-Schuhman D, Jimeno-Yepes A, Li C, Kafkas S, Lewin I, Kang N, Corbett P, Milward D, Buyko E, Beisswanger E, Hornbostel K, Kouznetsov A, Witte R, Laurila J, Baker C, Kuo CJ, Clematide S, Rinaldi F, Farkas R, Hahn U (2011) Assessment of ner solutions against the first and second calbc silver standard corpus. J Biomed Semant 2(5):1–12

    Google Scholar 

  112. Roberts RJ (2001) Pubmed central: the genbank of the published literature. Proc Natl Acad Sci 98:381–382

    Article  Google Scholar 

  113. Robson B, Boray S, Weisman J (2022) Mining real-world high dimensional structured data in medicine and its use in decision support. Some different perspectives on unknowns, interdependency, and distinguishability. Comput Biol Med 141:105,118

    Article  Google Scholar 

  114. Rokach L, Romano R, Maimon O (2008) Negation recognition in medical narrative reports. Inf Retr 11(6):499–538

    Article  Google Scholar 

  115. Rosario B, Hearst MA (2004) Classifying semantic relations in bioscience texts. In: Proceedings of the 42nd annual meeting of the association for computational linguistics (ACL-04), pp 430–437

  116. Rundo L, Pirrone R, Vitabile S, Sala E, Gambino O (2020) Recent advances of hci in decision-making tasks for optimized clinical workflows and precision medicine. J Biomed Inform 108(103):479

    Google Scholar 

  117. Sadoughi N, Finley GP, Edwards E, Robinson A, Korenevsky M, Brenndoerfer M, Axtmann N, Miller M, Suendermann-Oeft D (2018) Detecting section boundaries in medical dictations: toward real-time conversion of medical dictations to clinical reports. In: International conference on speech and computer, Springer, pp 563–573

  118. Shi J, Li W, Yang Y, Yao N, Bai Q, Yongchareon S, Yu J (2021) Automated concern exploration in pandemic situations-covid-19 as a use case. Pacific rim knowledge acquisition workshop. Springer, Cham, pp 178–185

    Google Scholar 

  119. Shi J, Li W, Yongchareon S, Yang Y, Bai Q (2022) Graph-based joint pandemic concern and relation extraction on twitter. Expert Syst Appl 195(116):538. https://doi.org/10.1016/j.eswa.2022.116538

    Article  Google Scholar 

  120. Sohrab MG, Duong K, Miwa M, Topić G, Masami I, Hiroya T (2020) Bennerd: a neural named entity linking system for covid-19. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pp 182–188

  121. Song HJ, Jo BC, Park CY, Kim JD, Kim YS (2018) Comparison of named entity recognition methodologies in biomedical documents. Biomed Eng Online 17(2):1–14

    Google Scholar 

  122. Sorgente A, Vettigli G, Mele F (2013) Automatic extraction of cause-effect relations in natural language text. DART @AI *IA 2013:37–48

    Google Scholar 

  123. Stubbs A, Kotfila C, Uzuner Ö (2015) Automated systems for the de-identification of longitudinal clinical narratives: overview of 2014 i2b2/uthealth shared task track 1. J Biomed Inform 58:S11–S19

    Article  Google Scholar 

  124. Stubbs A, Kotfila C, Xu H, Uzuner Ö (2015) Identifying risk factors for heart disease over time: overview of 2014 i2b2/uthealth shared task track 2. J Biomed Inform 58:S67–S77

    Article  Google Scholar 

  125. Sudeshna P, Bhanumathi S, Hamlin MA (2017) Identifying symptoms and treatment for heart disease from biomedical literature using text data mining. 2017 international conference on computation of power. Energy Information and Commuincation (ICCPEIC), IEEE, pp 170–174

    Google Scholar 

  126. Sui Y, Bu F, Hu Y, Yan W, Zhang L (2022) Trigger-gnn: a trigger-based graph neural network for nested named entity recognition. arXiv:2204.05518

  127. Sun Q, Bhatia P (2021) Neural entity recognition with gazetteer based fusion. Findings of the association for computational linguistics: ACL/IJCNLP 2021. Association for Computational Linguistics, Stroudsburg, Pennsylvania, pp 3291–3295

    Chapter  Google Scholar 

  128. Sun W, Cai Z, Liu F, Fang S, Wang G (2017) A survey of data mining technology on electronic medical records. 2017 IEEE 19th international conference on e-health networking. Applications and Services (Healthcom), IEEE, pp 1–6

    Google Scholar 

  129. Sun W, Cai Z, Li Y, Liu F, Fang S, Wang G (2018) Data processing and text mining technologies on electronic medical records: a review. J Healthc Eng. https://doi.org/10.1155/2018/4302425

    Article  Google Scholar 

  130. Sun W, Cai Z, Li Y, Liu F, Fang S, Wang G (2018) Security and privacy in the medical internet of things: a review. Secur Commun Netw 2018:1–30

    Google Scholar 

  131. Suominen HJ, Salakoski TI (2010) Supporting communication and decision making in finnish intensive care with language technology. J Healthc Eng 1(4):595–614

    Article  Google Scholar 

  132. Tang B, Cao H, Wu Y, Jiang M, Xu H (2013) Recognizing clinical entities in hospital discharge summaries using structural support vector machines with word representation features. BMC Med Inform Decis Mak BioMed Cent 13:1–10

    Article  Google Scholar 

  133. Tang J, Liu A, Zhao M, Wang T (2018) An aggregate signature based trust routing for data gathering in sensor networks. Secur Commun Netw 2018:1–30

    Article  Google Scholar 

  134. Tchraktchiev D, Angelova G, Boytcheva S, Angelov Z, Zacharieva S (2011) Completion of structured patient descriptions by semantic mining. Patient safety informatics. IOS Press, Amsterdam, pp 260–269

    Google Scholar 

  135. Tepper M, Capurro D, Xia F, Vanderwende L, Yetisgen-Yildiz M (2012) Statistical section segmentation in free-text clinical records. In: Lrec, pp 2001–2008

  136. Tran T, Kavuluru R (2019) Distant supervision for treatment relation extraction by leveraging mesh subheadings. Artif Intell Med 98:18–26

    Article  Google Scholar 

  137. Tran V, Tran VH, Nguyen P, Nguyen C, Satoh K, Matsumoto Y, Nguyen M (2021) Covrelex: a covid-19 retrieval system with relation extraction. In: Proceedings of the 16th conference of the european chapter of the association for computational linguistics: system demonstrations, pp 24–31

  138. Uzuner Ö, Solti I, Cadag E (2010) Extracting medication information from clinical text. J Am Med Inform Assoc 17(5):514–518

    Article  Google Scholar 

  139. Uzuner Ö, South BR, Shen S, DuVall SL (2011) 2010 i2b2/va challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 18(5):552–556

    Article  Google Scholar 

  140. Vunikili R, Supriya H, Marica VG, Farri O (2020) Clinical ner using spanish bert embeddings. In: IberLEF@ SEPLN, pp 505–511

  141. Wang L, Foer D, MacPhaul E, Lo YC, Bates DW, Zhou L (2021) Pasclex: a comprehensive post-acute sequelae of covid-19 (pasc) symptom lexicon derived from electronic health record clinical notes. J Biomed Inform 125:103951

    Article  Google Scholar 

  142. Wang P, Hao T, Yan J, Jin L (2017) Large-scale extraction of drug-disease pairs from the medical literature. J Assoc Inf Sci Technol 68(11):2649–2661

    Article  Google Scholar 

  143. Wang S, Ren F, Lu H (2018) A review of the application of natural language processing in clinical medicine. In: 2018 13th IEEE conference on industrial electronics and applications (ICIEA), pp 2725–2730

  144. Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, Liu S, Zeng Y, Mehrabi S, Sohn S, Liu H (2018) Clinical information extraction applications: a literature review. J Biomed Inform 77:34–49

    Article  Google Scholar 

  145. Wang Y, Fu S, Shen F, Henry S, Uzuner O, Liu H (2020) The 2019 n2c2/ohnlp track on clinical semantic textual similarity: overview. JMIR Med Inform 8(11):e23375

    Article  Google Scholar 

  146. Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H (2019) Relation extraction from clinical narratives using pre-trained language models. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2019, p 1236

  147. Weiskopf NG, Hripcsak G, Swaminathan S, Weng C (2013) Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform 46(5):830–836

    Article  Google Scholar 

  148. Wu Y, Jiang M, Xu J, Zhi D, Xu H (2017) Clinical named entity recognition using deep learning models. In: AMIA annual symposium proceedings, American Medical Informatics Association, vol 2017, p 1812

  149. Xia J, Cai Z, Hu G, Xu M (2019) An active defense solution for arp spoofing in openflow network. Chin J Electron 28(1):172–178

    Article  Google Scholar 

  150. Xu J, Gan L, Cheng M, Wu Q (2018) Unsupervised medical entity recognition and linking in chinese online medical text. J Healthc Eng. https://doi.org/10.1155/2018/2548537

    Article  Google Scholar 

  151. Yang J, Han SC, Poon J (2021a) A survey on extraction of causal relations from natural language text. CoRR abs/2101.06426, arXiv:2101.06426

  152. Yang L, Cai ZP, Xu H (2018) Llmp: exploiting lldp for latency measurement in software-defined data center networks. J Comput Sci Technol 33(2):277–285

    Article  Google Scholar 

  153. Yang X, Zhang H, He X, Bian J, Wu Y (2020) Extracting family history of patients from clinical narratives: exploring an end-to-end solution with deep learning models. JMIR Med Inform 8(12):e22982

    Article  Google Scholar 

  154. Yang X, Yu Z, Guo Y, Bian J, Wu Y (2021b) Clinical relation extraction using transformer-based models. CoRR abs/2107.08957, arXiv:2107.08957

  155. Yang Z, Lin H, Li Y (2008) Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature. Comput Biol Chem 32(4):287–291

    Article  MATH  Google Scholar 

  156. Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: generalized autoregressive pretraining for language understanding. Adv Neural Inf Process Syst 32:1–10

    Google Scholar 

  157. Zhang H, Cai Z, Liu Q, Xiao Q, Li Y, Cheang CF (2018) A survey on security-aware measurement in sdn. Secur Commun Netw. https://doi.org/10.1155/2018/2459154

    Article  Google Scholar 

  158. Zhang R, Chu F, Chen D, Shang X (2018) A text structuring method for chinese medical text based on temporal information. Int J Environ Res Public Health 15(3):402

    Article  Google Scholar 

  159. Zhang S, Elhadad N (2013) Unsupervised biomedical named entity recognition: experiments with clinical and biological texts. J Biomed Inform 46(6):1088–1098

    Article  Google Scholar 

  160. Zhang Y, Yan X, Gao X, Chen Q, Hu HP (2016) Demand analysis of decision support system of grass-roots health. Chin Gen Pract 19:2636–2639. https://doi.org/10.3969/j.issn.1007-9572.2016.22.005

    Article  Google Scholar 

  161. Zhao X, Ding H, Feng Z (2021) Glara: graph-based labeling rule augmentation for weakly supervised named entity recognition. In: Proceedings of the 16th conference of the european chapter of the association for computational linguistics: main volume, EACL 2021, association for computational linguistics, pp 3636–3649. 10.18653/v1/2021.eacl-main.318

  162. Zheng J, Chapman WW, Crowley RS, Savova GK (2011) Coreference resolution: a review of general methodologies and applications in the clinical domain. J Biomed Inform 44(6):1113–1122

    Article  Google Scholar 

  163. Zhou Y, Ju C, Caufield JH, Shih K, Chen CY, Sun Y, Chang K, Ping P, Wang W (2021) Clinical named entity recognition using contextualized token representations. CoRR abs/2106.12608, arXiv:2106.12608

  164. Zweigenbaum P, Deléger L, Lavergne T, Névéol A, Bodnari A (2013) A supervised abbreviation resolution system for medical text. In: CLEF (Working Notes)

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. MARS Research Laboratory, SDM Research Group, ISITCom, University of Sousse, Hammam Sousse, Tunisia

    Mohamed Yassine Landolsi, Lobna Hlaoua & Lotfi Ben Romdhane

Authors
  1. Mohamed Yassine Landolsi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lobna Hlaoua
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lotfi Ben Romdhane
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

M.Y.L. analyzed the literature and wrote the manuscript. All authors read, revised and approved the final manuscript.

Corresponding author

Correspondence to Mohamed Yassine Landolsi.

Ethics declarations

Abbreviations

Not applicable.

Ethics approval and consent to participate

Not applicable.

Conflict of interest

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Authors’ information

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Landolsi, M.Y., Hlaoua, L. & Ben Romdhane, L. Information extraction from electronic medical documents: state of the art and future research directions. Knowl Inf Syst 65, 463–516 (2023). https://doi.org/10.1007/s10115-022-01779-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01779-1

Keywords