PDF herunterladen
Yearb Med Inform 2015; 24(01): 183-193
DOI: 10.15265/IY-2015-009
Original Article
Georg Thieme Verlag KG Stuttgart

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

S. Velupillai
1   Department of Computer and Systems Sciences, (DSV), Stockholm University, Stockholm, Sweden
,
D. Mowery
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
,
B. R. South
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
,
M. Kvist
1   Department of Computer and Systems Sciences, (DSV), Stockholm University, Stockholm, Sweden
3   Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Sweden
,
H. Dalianis
1   Department of Computer and Systems Sciences, (DSV), Stockholm University, Stockholm, Sweden
› Institutsangaben
Weitere Informationen

Publikationsverlauf

13. August 2015

Publikationsdatum:
10. März 2018 (online)

   Lizenzen und Reprints

Summary

Objectives: We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis.

Methods: We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers.

Results: Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications.

Conclusions: There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.

Keywords

Clinical Natural Language Processing - Semantics - Information Extraction - Annotation, Domain Adaptation - Review
 

  • References

  • 1 Zeng QT, Redd D, Divita G, Jarad S, Brandt C. Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes. J Health Med Inform 2011; S3: 001.
    PubMedGoogle Scholar
  • 2 Friedman C, Kra P, Rzhetsky A. Two biomedical sublanguages: a description based on the theories of Zellig Harris. J Biomed Inform 2002; 35 (04) 222-35.
    CrossrefPubMedGoogle Scholar
  • 3 Temnikova I, Nikolova I, Baumgartner Jr WA, Angelova G, Cohen KB. Closure Properties of Bulgarian Clinical Text. In Proc RANLP 2013; September: 667-75.
    PubMedGoogle Scholar
  • 4 Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008; 128-44.
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 5 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; SepOct 18 (05) 540-3.
    CrossrefPubMedGoogle Scholar
  • 6 Névéol A H, Dalianis G, Savova, Zweigenbaum P. Didactic Panel: Clinical Natural Language Processing in Languages Other Than English. In: Proc AMIA Annu Symp; 2014
    Google Scholar
  • 7 Zweigenbaum P, Névéol A. Section Editors for the IMIA Yearbook section on clinical natural language processing. Clinical NLP synopsis. Yearb Med Inform 2014 to appear
    PubMedGoogle Scholar
  • 8 Cohen KB, Demner-Fushman D. Biomedical natural language processing (Vol. 11). John Benjamins Publishing Company; 2014
    Google Scholar
  • 9 Hripcsak G, Rothschild AS. Agreement, the F-measure, and reliability in information retrieval. J Am Med Inform Assoc 2005; May-Jun; 12 (03) 296-8.
    CrossrefPubMedGoogle Scholar
  • 10 Pustejovsky J, Stubbs A. Natural Language Annotation for Machine Learning - A Guide to Corpus-Building for Applications. O’Reilly Media; 2012
    Google Scholar
  • 11 UMLS Metathesaurus: http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/ [16 June 2015]
    PubMed
  • 12 Lingren T, Deleger L, Molnar K, Zhai H, MeinzenDerr J, Kaiser M. et al. Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements. J Am Med Inform Assoc 2014; May-Jun 21 (03) 406-13.
    CrossrefPubMedGoogle Scholar
  • 13 Gobbel GT, Garvin J, Reeves R, Cronin RM, Heavirland J, Williams J. et al. Assisted annotation of medical free text using RapTAT. J Am Med Inform Assoc 2014; Sep-Oct 21 (05) 833-41.
    CrossrefPubMedGoogle Scholar
  • 14 South BR, Mowery D, Suo Y, Leng J, Ferrández Ó, Meystre SM. et al. Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text. J Biomed Inform 2014; Aug 50: 162-72.
    CrossrefPubMedGoogle Scholar
  • 15 Grouin C, Lavergne T, Neveol A. Optimizing annotation efforts to build reliable annotated corpora for training statistical models. The 8th Linguistic Annotation Workshop, 2-14 August 2014. ACL; 2014 p. 54-8.
    PubMedGoogle Scholar
  • 16 Zhai H, Lingren T, Deleger L, Li Q, Kaiser M, Stoutenborough L. et al. Web 2.0-based crowd-sourcing for high-quality gold standard development in clinical natural language processing. J Med Internet Res 2013; Apr 2 15 (04) e73.
    CrossrefPubMedGoogle Scholar
  • 17 Huang CC, Lu Z. Community challenges in biomedical text mining over 10 years: success, failure and the future. Brief Bioinform; 2015. May 1.
    Google Scholar
  • 18 Uzuner O, Solti, Cadag E. Extracting medication information from clinical text. J Am Med Inform Assoc 2010; Sep-Oct 17 (05) 514-8.
    CrossrefPubMedGoogle Scholar
  • 19 Juckett D. A method for determining the number of documents needed for a gold standard corpus. J Biomed Inform 2012; Jun 45 (03) 460-70.
    CrossrefPubMedGoogle Scholar
  • 20 Gundlapalli AV, Redd A, Carter M, Divita G, Shen S, Palmer M. et al. Validating a strategy for psychosocial phenotyping using a large corpus of clinical text. J Am Med Inform Assoc 2013; Dec 20 e2 e355-64.
    CrossrefPubMedGoogle Scholar
  • 21 Meystre SM, Friedlin FJ, South BR, Shen S, Samore MH. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Med Res Methodol 2010; 10 (01) 70.
    CrossrefPubMedGoogle Scholar
  • 22 HIPAA: http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/index.html [14 December 2014]
    PubMed
  • 23 Shin SY, Lyu Y, Shin Y, Choi HJ, Park J, Kim WS. et al. Lessons Learned from Development of De-identification System for Biomedical Research in a Korean Tertiary Hospital. Healthc Inform Res 2013; Jun 19 (02) 102-9.
    CrossrefPubMedGoogle Scholar
  • 24 Uzuner Ö, Luo Y. Szolovits, Evaluating the state-of-the-art in automatic de-identification. J Am Med Inform Assoc 2007; Sep-Oct; 14 (05) 550-63.
    CrossrefPubMedGoogle Scholar
  • 25 Ferrández O, South BR, Shen S, Friedlin FJ, Samore MH, Meystre SM. BoB, a best-of breed automated text de-identification system for VHA clinical documents. J Am Med Inform Assoc 2013; 20: 77-83.
    CrossrefPubMedGoogle Scholar
  • 26 Ferrández O, South BR, Shen S, Meystre SM. A hybrid stepwise approach for de-identifying person names in clinical documents. In: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing. 2012 June. ACL; 2012. p. 65-72.
    Google Scholar
  • 27 Meystre S, Ferrández O, Friedlin J, South BR, Shen S, Samore MH. Text de-identification for privacy protection : A study of its impact on clinical text information content. J Biomed Inform 2014; 50: 140-50.
    PubMedGoogle Scholar
  • 28 Li M, Carrell D, Aberdeen J, Hirschman L, Malin BA. De-identification of clinical narratives through writing complexity measures. Int J Med Inform 2014; Oct 83 (10) 750-67.
    CrossrefPubMedGoogle Scholar
  • 29 Grouin C, Névéol A. De-identification of clinical notes in French: towards a protocol for reference corpus development. J Biomed Inform 2014; Aug 50: 151-61.
    CrossrefPubMedGoogle Scholar
  • 30 Dalianis H, Velupillai S. De-identifying Swedish clinical text - refinement of a gold standard and experiments with Conditional random fields. J Biomed Semantics 2010; Apr 12 1 (01) 6.
    CrossrefPubMedGoogle Scholar
  • 31 Alfalahi A, Brissman S, Dalianis H. Pseudonymisation of person names and other PHIs in an annotated clinical Swedish corpus. In: Proceedings of the Third Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2012) held in conjunction with LREC. 2012, May 26, Istanbul: P. 49-54.
    Google Scholar
  • 32 Deleger L, Molnar K, Savova G, Xia F, Lingren T, Li Q. et al. Large-scale evaluation of automated clinical note de-identification and its impact on information extraction. J Am Med Inform Assoc 2013; Jan 1 20 (01) 84-94.
    CrossrefPubMedGoogle Scholar
  • 33 Sánchez D, Batet M, Viejo A. Utility-preserving privacy protection of textual healthcare documents. J Biomed Inform 2014; Dec 52: 189-98.
    CrossrefPubMedGoogle Scholar
  • 34 Fan JW, Yang EW, Jiang M, Prasad R, Loomis RM, Zisook DS. et al. Syntactic parsing of clinical text: guideline and corpus development with handling ill-formed sentences. J Am Med Inform Assoc 2013; Nov-Dec 20 (06) 1168-77.
    CrossrefPubMedGoogle Scholar
  • 35 Penn Treebank II guidelines: ftp://ftp.cis.upenn.edu/pub/treebank/doc/manual/root.ps.gz [16 June 2015]
    PubMed
  • 36 Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC. et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010; Sep-Oct 17 (05) 507-13.
    CrossrefPubMedGoogle Scholar
  • 37 Ferraro JP, Daumé 3rd 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; Sep-Oct 20 (05) 931-9.
    CrossrefPubMedGoogle Scholar
  • 38 Laippala V, Viljanen T, Airola A, Kanerva J, Salanterä S, Salakoski T. et al. Statistical parsing of varieties of clinical Finnish. Artif Intell Med 2014; Jul 61 (03) 131-6.
    CrossrefPubMedGoogle Scholar
  • 39 Bretschneider C, Zillner S, Hammon M. Identifying pathological findings in German radiology reports using a syntacto-semantic parsing approach. In: Proceedings of BioNLP 2013. ACL; 2013. p. 27-35.
    Google Scholar
  • 40 Albright D, Lanfranchi A, Fredriksen A, Styler 4th WF, Warner C, Hwang JD. et al. Towards comprehensive syntactic and semantic annotations of the clinical narrative. J Am Med Inform Assoc 2013; Sep-Oct 20 (05) 922-30.
    CrossrefPubMedGoogle Scholar
  • 41 Wu ST, Kaggal VC, Dligach D, Masanz JJ, Chen P, Becker L. et al. A common type system for clinical natural language processing. J Biomed Semantics 2013; Jan 3 4 (01) 1.
    CrossrefPubMedGoogle Scholar
  • 42 CEM: http://wiki.siframework.org/file/view/CEReference20081114.pdf [16 June 2015]
    PubMed
  • 43 Uzuner Ö, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011; Sep-Oct 18 (05) 552-6.
    CrossrefPubMedGoogle Scholar
  • 44 Pradhan S, Elhadad N, South BR, Martinez D, Christensen L, Vogel A. et al. Evaluating the state of the art in disorder recognition and normalization of the clinical narrative. J Am Med Inform Assoc 2014 Aug 21.
    PubMedGoogle Scholar
  • 45 Elhadad N, Pradhan S, Lipsky Gorman S, Manandhar S, Chapman W. et al. SemEval-2015 Task 14: Analysis of Clinical Text. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). ACL; 2015
    Google Scholar
  • 46 Skeppstedt M, Kvist M, Nilsson GH, Dalianis H. Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study. J Biomed Inform 2014; Jun 49: 148-58.
    CrossrefPubMedGoogle Scholar
  • 47 MIMIC II: https://mimic.physionet.org/database.html [16 June 2015]
    PubMed
  • 48 Mowery D, Velupillai S, South BR, Christensen L, Martinez D, Kelly L. et al. Task 2: ShARe/CLEF eHealth Evaluation Lab 2014. In: CEUR Workshop Proceedings; 2014: 1180.
    Google Scholar
  • 49 Dligach D, Bethard S, Becker L, Miller T, Savova GK. Discovering body site and severity modifiers in clinical texts. J Am Med Inform Assoc 2014; May-Jun 21 (03) 448-54.
    CrossrefPubMedGoogle Scholar
  • 50 Xu J, Zhang Y, Wang J, Wu Y, Jiang M, Soysal E. et al. UTH-CCB: The Participation of the SemEval 2015 Challenge – Task 14. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). ACL; 2015
    Google Scholar
  • 51 Chapman B, Lee S, Kang H, Chapman W. Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm. J Biomed Inform 2011; 44: 728-37.
    CrossrefPubMedGoogle Scholar
  • 52 Velupillai S, Skeppstedt M, Kvist M, Mowery D, Chapman BE, Dalianis H, Chapman WW. Cue-based assertion classification for Swedish clinical text-developing a lexicon for pyConTextSwe. Artif Intell Med 2014; Jul 61 (03) 137-44.
    CrossrefPubMedGoogle Scholar
  • 53 Chapman WW, Hillert D, Velupillai S, Kvist M, Skeppstedt M, Chapman BE. et al. Extending the NegEx lexicon for multiple languages. Stud Health Technol Inform 2013; 192: 677-81.
    PubMedGoogle Scholar
  • 54 Mowery DL, Velupillai S, Chapman WW. Medical diagnosis lost in translation: analysis of uncertainty and negation expressions in English and Swedish clinical texts. In: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing. 2012 June. ACL; 2012. p. 56-64.
    Google Scholar
  • 55 Afzal Z, Pons E, Kang N, Sturkenboom MC, Schuemie MJ, Kors JA. ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus. BMC Bioinformatics 2014; Nov 29 15 (01) 373.
    CrossrefPubMedGoogle Scholar
  • 56 Cogley J, Stokes N, Carthy J, Dunnion J. Analyzing patient records to establish if and when a patient suffered from a medical condition. In: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing. 2012 June. ACL; 2012. p. 38-46.
    Google Scholar
  • 57 Uzuner O, Bodnari A, Shen S, Forbush T, Pestian J, South BR. Evaluating the state of the art in coreference resolution for electronic medical records. J Am Med Inform Assoc 2012; SepOct 19 (05) 786-91.
    CrossrefPubMedGoogle Scholar
  • 58 Bodnari A, Szolovits P, Uzuner Ö. MCORES: a system for noun phrase coreference resolution for clinical records. J Am Med Inform Assoc 2012; Sep-Oct 19 (05) 906-12.
    CrossrefPubMedGoogle Scholar
  • 59 Chowdhury MF, Zweigenbaum P. A controlled greedy supervised approach for co-reference resolution on clinical text. J Biomed Inform 2013; Jun 46 (03) 506-15.
    CrossrefPubMedGoogle Scholar
  • 60 Jindal P, Roth D. Using domain knowledge and domain-inspired discourse model for coreference resolution for clinical narratives. J Am Med Inform Assoc 2013; Mar-Apr; 20 (02) 356-62.
    CrossrefPubMedGoogle Scholar
  • 61 Pustejovsky J, Lee K, Bunt H, Romary L. ISOTimeML: An International Standard for Semantic Annotation. In: Proc 7th Intl Conference on Language Resources and Evaluation (LREC’10). 2012. May, Valletta, Malta: ELRA;
    Google Scholar
  • 62 Styler WF, Bethard S, Finan S, Palmer M, Pradhan S, de Groen P. et al. Temporal annotation in the clinical domain. Transactions of the Association for Computational Linguistics 2014; 2: 143-54.
    PubMedGoogle Scholar
  • 63 Sun W, Rumshisky A, Uzuner O. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. J Am Med Inform Assoc 2013; SepOct 20 (05) 806-13.
    CrossrefPubMedGoogle Scholar
  • 64 Sun W, Rumshisky A, Uzuner O. Temporal reasoning over clinical text: the state of the art. J Am Med Inform Assoc 2013; Sep-Oct 20 (05) 814-9.
    CrossrefPubMedGoogle Scholar
  • 65 Bethard S, Derczynski L, Savova G, Pustejovsky J, Verhagen M. SemEval-2015 Task 6: Clinical TempEval. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). ACL;
    Google Scholar
  • 66 Velupillai S, Mowery DL, Abdelrahman S, Christensen L, Chapman W. BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). ACL.;
    Google Scholar
  • 67 Verhagen M, Mani I, Sauri R. et al. Automating temporal annotation with TARSQI. Proceedings of the ACL 2005 on Interactive Poster and Demonstration Sessions. ACL; 2005. p. 81-4.
    Google Scholar
  • 68 Reeves RM, Ong FR, Matheny ME, Denny JC, Aronsky D, Gobbel GT. et al. Detecting temporal expressions in medical narratives. Int J Med Inform 2013; Feb 82 (02) 118-27.
    CrossrefPubMedGoogle Scholar
  • 69 Strötgen J, Gertz M. Multilingual and Cross-domain Temporal Tagging. Language Resources and Evaluation 2013; 47 (02) 269-98.
    CrossrefPubMedGoogle Scholar
  • 70 Chang AX, Manning CD. SUTIME: a library for recognizing and normalizing time expressions. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12) 2012. ELRA; 2012
    Google Scholar
  • 71 Raghavan P, Fosler-Lussier E, Lai AM. Temporal classification of medical events. In: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing. 2012 June. ACL; 2012. p. 29-37.
    Google Scholar
  • 72 Boytcheva S, Angelova G, Nikolova I. Automatic Analysis of Patient History Episodes in Bulgarian Hospital Discharge Letters. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Avignon, France: April 23 - 27 2012. ACL; 2012. p. 77-81.
    Google Scholar
  • 73 Raghavan P, Fossler-Lussier E, Elhadad N, Lai A. M. Cross-narrative temporal ordering of medical events. In: Proc. ACL; 2014. ACL; 2014. p. 998-1008.
    Google Scholar
  • 74 Pestian JP, Matykiewicz P, Linn-Gust M. What’s In a Note: Construction of a Suicide Note Corpus. Biomed Inform Insights 2012; 5: 1-6.
    CrossrefPubMedGoogle Scholar
  • 75 Pestian JP, Matykiewicz P, Linn-Gust M, South B, Uzuner O, Wiebe J. et al. Sentiment Analysis of Suicide Notes: A Shared Task. Biomed Inform Insights 2012; 5 (01) 3-16.
    PubMedGoogle Scholar
  • 76 TREC Medical track: http://www-nlpir.nist.gov/projects/trecmed/2011/tm2011.html [16 June 2015]
    PubMed
  • 77 Voorhees EM, Hersh W. Overview of the TREC 2012 Medical Records Track. In: Proc. TREC; 2012
    Google Scholar
  • 78 Wu Y, Lei J, Wei WQ, Tang B, Denny JC, Rosen-bloom ST. et al. Analyzing differences between Chinese and English clinical text: a cross-institution comparison of discharge summaries in two languages. Stud Health Technol Inform 2013; 192: 662-6.
    PubMedGoogle Scholar
  • 79 Pivovarov R, Elhadad N. Automated Methods for the Summarization of Electronic Health Records. J Am Med Inform Assoc 2015
    PubMedGoogle Scholar
  • 80 Matykiewicz P, Cohen KB, Holland KD, Glauser TA, Standridge SM, Verspoor KM, Pestian J. Earlier Identification of Epilepsy Surgery Candidates Using Natural Language Processing. In: Proceedings of the 2013 Workshop on Biomedical Natural Language Processing. ACL; 2013
    Google Scholar
  • 81 Leeper NJ, Bauer-Mehren A, Iyer SV, LePendu P, Olson C, Shah NH. Practice-Based Evidence: Profiling the Safety of Cilostazol by Text-Mining of Clinical Notes. PLoS ONE 2013; 8 (05) e63499.
    CrossrefPubMedGoogle Scholar
  • 82 Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012; jun 13 (06) 395-405.
    CrossrefPubMedGoogle Scholar
  • 83 Shivade C, Raghavan P, Fosler-Lussier E, Elhadad N, Johnson SB, Lai AM. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc 2014; 21 (02) 221-30.
    PubMedGoogle Scholar
  • 84 Wright A, McCoy AB, Henkin S, Kale A, Sittig DF. Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions. J Am Med Inform Assoc 2013; SepOct 20 (05) 887-90.
    CrossrefPubMedGoogle Scholar
  • 85 Yetisgen-Yildiz M, Bejan CA, Wurfel MM. Identification of Patients with Acute Lung Injury from Free-Text Chest X-Ray Reports. In: Proceedings of the 2013 Workshop on Biomedical Natural Language Processing. ACL; 2013
    Google Scholar
  • 86 Zeng QT, Goryachev S, Weiss S, Sordo M, Murphy SN, Lazarus R. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006; Jul 26 6: 30.
    CrossrefPubMedGoogle Scholar
  • 87 Perlis RH, Iosifescu DV, Castro VM, Murphy SN, Gainer VS, Minnier J. et al. Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med 2012; Jan 42 (01) 41-50.
    CrossrefPubMedGoogle Scholar
  • 88 Spasić I, Livsey J, Keane JA, Nenadić G. Text mining of cancer-related information: review of current status and future directions. Int J Med Inform 2014; Sep 83 (09) 605-23.
    CrossrefPubMedGoogle Scholar
  • 89 ICD: http://www.who.int/classifications/icd/en [16 June 2015]
    PubMed
  • 90 Socialstyrelsen 2006. Socialstyrelsen - The National Board of Health and Welfare, Diagnosgranskningar utförda i Sverige 1997-2005 samt råd inför granskning, [Diagnostic reviews performed in Sweden 1997-2005 and advice for reviews] (In Swedish). http://www.socialstyrelsen.se/Lists/Artikelkatalog/Attachments/9740/2006-131-30_200613131.pdf [12 June 2015]
    PubMedGoogle Scholar
  • 91 Stanfill MH, Williams M, Fenton SH, Jenders RA, Hersh WR. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc 2010; 17 (06) 646-51.
    CrossrefPubMedGoogle Scholar
  • 92 Perotte A, Pivovarov R, Natarajan K, Weiskopf N, Wood F, Elhadad N. Diagnosis code assignment: models and evaluation metrics. J Am Med Inform Assoc 2014; 21 (02) 231-7.
    CrossrefPubMedGoogle Scholar
  • 93 Martinez D, Cavedon L, Alam Z, Bain C, Verspoor K. Text mining for lung cancer cases over large patient admission data. In: Proceedings of the Abstracts of the Scientific Stream at Big Data. 2014. CEUR Proceedings.; p. 24-5.
    Google Scholar
  • 94 Li Q, Melton K, Lingren T, Kirkendall ES, Hall E, Zhai H. et al. Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care. J Am Med Inform Assoc 2014; Sep-Oct 21 (05) 776-84.
    CrossrefPubMedGoogle Scholar
  • 95 Eriksson R, Bjødstrup Jensen P, Frankild S, Juhl Jensen L, Brunak S. Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text. J Am Med Inform Assoc 2013; 20 (05) 947-53.
    CrossrefPubMedGoogle Scholar
  • 96 Aramaki E, Miura Y, Tonoike M, Ohkuma T, Masuichi H, Waki K. et al. Extraction of adverse drug effects from clinical records. Stud Health Technol Inform 2010; 160 Pt 1 739-43.
    PubMedGoogle Scholar
  • 97 Freeman R, Moore LSP, García Álvarez L, Charlett A, Holmes A. Advances in electronic surveillance for healthcare-associated infections in the 21st Century: a systematic review. J Hosp Infect 2013; 84 (02) 106-19.
    CrossrefPubMedGoogle Scholar
  • 98 EU HAI Guidelines: http://www.ecdc.europa.eu/en/activities/surveillance/HAI/Pages/default.aspx [16 June 2015]
    PubMed
  • 99 USA HAI Guidelines: http://www.health.gov/hai/prevent_hai.asp [16 June 2015]
    PubMed
  • 100 Shepard J, Hadhazy E, Frederick J, Nicol S, Gade P, Cardon A. et al. Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting. Am J of Infection Control 2014; 42: e33-e36.
    PubMedGoogle Scholar
  • 101 Ehrentraut C, Kvist M, Sparrelid M, Dalianis H. Detecting Healthcare-Associated Infections in Electronic Health Records - Evaluation of Machine Learning and Preprocessing Techniques, in the Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014). 2014. Aveiro, Portugal:
    Google Scholar
  • 102 Proux D, Hagège C, Gicquel Q, Pereira S, Darmoni S, Segond F. et al. Architecture and systems for monitoring hospital acquired infections inside a hospital information workflow. In Proceedings of the Workshop on Biomedical Natural Language Processing; 2011. September, USA: Portland, Oregon;
    Google Scholar
  • 103 Adlassnig K-P, Blacky A, Koller W. Artificial-Intelligence-Based Hospital- Acquired Infection Control. Strategy for the Future of Health. Stud Health Technol Inform 2009; 149: 103-10.
    PubMedGoogle Scholar
  • 104 Meaningful use: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Downloads/eQuality_EHR_TEP_ Findings.pdf [16 June 2015]
    PubMed
  • 105 The White House Office of the Press Secretary.. FACT SHEET: President Obama’s Precision Medicine Initiative. 2015 Jan 30. https://www.whitehouse.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative [25 June 2015]
    PubMedGoogle Scholar
  • 106 eHealth Action Plan 2012-2020 - Innovative healthcare for the 21st century. COM/2012/0736 final http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52012DC0736&-from=EN [16 June 2015]
    PubMedGoogle Scholar