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Clinical Text Mining pp 109–148Cite as

Applications of Clinical Text Mining

Applications of Clinical Text Mining

  • Hercules Dalianis2 
  • Chapter
  • Open Access
  • First Online: 15 May 2018

  • 18k Accesses

  • 1 Altmetric

Abstract

This chapter presents various applications of clinical text mining that all use the electronic patient record text as input data.

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References

  • Andersson, P., & Sjöberg, A. (2016). Generating and Evaluating an Automatic Mapping Between SNOMED-CT and the Swedish Extension Codes of ICD-10 Based on Lexical Similarities. Master’s thesis, Department of Computer and Systems Sciences, Stockholm University.

    Google Scholar 

  • Aramaki, E., Miura, Y., Tonoike, M., Ohkuma, T., Mashuichi, H., & Ohe, K. (2009). Text2table: Medical text summarization system based on named entity recognition and modality identification. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (pp. 185–192). Association for Computational Linguistics.

    Google Scholar 

  • Aramaki, E., Miura, Y., Tonoike, M., Ohkuma, T., Masuichi, H., Waki, K., et al. (2010). Extraction of adverse drug effects from clinical records. Studies in Health Technology and Informatics, 160(Pt 1), 739–743.

    Google Scholar 

  • Aramaki, E., Morita, M., Kano, Y., & Ohkuma, T. (2014). Overview of the NTCIR-11 MedNLP-2 Task. In NTCIR.

    Google Scholar 

  • Bailey, C., Peddie, D., Wickham, M. E., Badke, K., Small, S. S., Doyle-Waters, M. M., et al. (2016). Adverse drug event reporting systems: A systematic review. British Journal of Clinical Pharmacology, 82(1), 17–29.

    CrossRef  Google Scholar 

  • Barak-Corren, Y., Castro, V. M., Javitt, S., Hoffnagle, A. G., Dai, Y., Perlis, R. H., et al. (2016). Predicting suicidal behavior from longitudinal electronic health records. American Journal of Psychiatry, 174(2), 154–162.

    CrossRef  Google Scholar 

  • Beijer, H. J. M., & de Blaey, C. J. (2002). Hospitalisations caused by adverse drug reactions (ADR): A meta-analysis of observational studies. Pharmacy World and Science, 24(2), 46–54.

    CrossRef  Google Scholar 

  • Blacky, A., Mandl, H., Adlassnig, K.-P., & Koller, W. (2011). Fully automated surveillance of healthcare-associated infections with MONI-ICU - A Breakthrough in clinical infection surveillance. Applied Clinical Informatics, 2(3), 365–372.

    Google Scholar 

  • Boytcheva, S. (2011). Automatic matching of ICD-10 codes to diagnoses in discharge letters. In Proceedings of the Workshop on Biomedical Natural Language Processing (pp. 11–18).

    Google Scholar 

  • Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-Based Expert Systems (Vol. 3). Reading, MA: Addison-Wesley.

    Google Scholar 

  • Buckley, J. M., Coopey, S. B., Sharko, J., Polubriaginof, F., Drohan, B., Belli, A. K., et al. (2012). The feasibility of using natural language processing to extract clinical information from breast pathology reports. Journal of Pathology Informatics, 3(1), 23.

    CrossRef  Google Scholar 

  • Casillas, A., Pérez, A., Oronoz, M., Gojenola, K., & Santiso, S. (2016). Learning to extract adverse drug reaction events from electronic health records in Spanish. Expert Systems with Applications, 61, 235–245.

    CrossRef  Google Scholar 

  • Chen, Y., Argentinis, J. D. E., & Weber, G. (2016). IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research. Clinical Therapeutics, 38(4), 688–701.

    CrossRef  Google Scholar 

  • Coden, A., Savova, G., Sominsky, I., Tanenblatt, M., Masanz, J., Schuler, K., et al. (2009). Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. Journal of Biomedical Informatics, 42(5), 937–949.

    CrossRef  Google Scholar 

  • Currie, A.-M., Fricke, T., Gawne, A., Johnston, R., Liu, J., & Stein, B. (2006). Automated extraction of free-text from pathology reports. In AMIA Annual Symposium Proceedings.

    Google Scholar 

  • Dahl, A., Özkan, A., & Dalianis, H. (2016). Pathology text mining-on Norwegian prostate cancer reports. In 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) (pp. 84–87). New York: IEEE.

    Google Scholar 

  • Dalianis, H. (2014). Clinical text retrieval - An overview of basic building blocks and applications. In Professional Search in the Modern World (pp. 147–165). Berlin: Springer.

    Google Scholar 

  • Dalianis, H., Hassel, M., & Velupillai, S. (2009). The Stockholm EPR Corpus-characteristics and some initial findings. In Proceedings of ISHIMR 2009, Evaluation and Implementation of e-Health and Health Information Initiatives: International Perspectives. 14th International Symposium for Health Information Management Research (pp. 243–249).

    Google Scholar 

  • Decker, A. (2003). Towards Automatic Grammatical Simplification of Swedish Text. Master’s thesis, Computational Linguistics, Department of Linguistics, Stockholm University.

    Google Scholar 

  • Doupi, P., Svaar, H., Bjørn, B., Deilkås, E., Nylén, U., & Rutberg, H. (2015). Use of the global trigger tool in patient safety improvement efforts: Nordic experiences. Cognition, Technology & Work, 17(1), 45–54.

    CrossRef  Google Scholar 

  • Downs, J., Velupillai, S., Gkotsis, G., Holden, R., Kikoler, M., Dean, H., et al. (2017). Detection of suicidality in adolescents with autism spectrum disorders: Developing a natural language processing approach for use in electronic health records. In AMIA Annual Symposium Proceedings.

    Google Scholar 

  • Ducel, G., Fabry, J., & Nicolle, L. (Eds.). (2002). Prevention of Hospital Acquired Infections: A Practical Guide., 2nd edn. World Health Organization. http://www.who.int/csr/resources/publications/drugresist/WHO_CDS_CSR_EPH_2002_12/en/. Accessed 11 Jan 2018.

  • Edwards, I. R., & Aronson, J. K. (2000). Adverse drug reactions: Definitions, diagnosis, and management. The Lancet, 356(9237), 1255–1259.

    CrossRef  Google Scholar 

  • Ehrentraut, C., Ekholm, M., Tanushi, H., Tiedemann, J., & Dalianis, H. (2016). Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting. Health Informatics Journal, 24(1), 24–42.

    CrossRef  Google Scholar 

  • Ehrentraut, C., Kvist, M., Sparrelid, E., & Dalianis, H. (2014). Detecting healthcare-associated infections in electronic health records: Evaluation of machine learning and preprocessing techniques. In Sixth International Symposium on Semantic Mining in Biomedicine (SMBM 2014). University of Aveiro.

    Google Scholar 

  • Elhadad, N., McKeown, K., Kaufman, D. R., & Jordan, D. A. (2005). Facilitating physicians’ access to information via tailored text summarization. In AMIA Annual Symposium Proceedings. Citeseer.

    Google Scholar 

  • Eriksson, R., Jensen, P. B., Frankild, S., Jensen, L. J., & Brunak, S. (2013). Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text. Journal of the American Medical Informatics Association, 20(5), 947–953.

    CrossRef  Google Scholar 

  • Falkenjack, J., Fahlborg, D., Rennes, E., Johansson, V., & Jönsson, A. (2017). Services for text simplification and analysis. In Proceedings of NODALIDA ’17 - 21th Nordic Conference on Computational Linguistics.

    Google Scholar 

  • Farkas, R., & Szarvas, G. (2008). Automatic construction of rule-based ICD-9-CM coding systems. BMC Bioinformatics, 9(3), S10.

    CrossRef  Google Scholar 

  • Forster, A. J., Jennings, A., Chow, C., Leeder, C., & van Walraven, C. (2012). A systematic review to evaluate the accuracy of electronic adverse drug event detection. Journal of the American Medical Informatics Association, 19(1), 31–38.

    CrossRef  Google Scholar 

  • Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease, 49(2), 407–422.

    CrossRef  Google Scholar 

  • Freeman, R., Moore, L. S. P., Álvarez, L. G., Charlett, A., & Holmes, A. (2013). Advances in electronic surveillance for healthcare-associated infections in the 21st century: A systematic review. Journal of Hospital Infection, 84(2), 106–119.

    CrossRef  Google Scholar 

  • Friedrich, S., & Dalianis, H. (2015). Adverse drug event classification of health records using dictionary-based pre-processing and machine learning. In Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis, Louhi, Held in Conjunction with EMNLP 2015, Lisbon, Portugal (pp. 121–130).

    Google Scholar 

  • Gerdes, L. U., & Hardahl, C. (2012). Text mining electronic health records to identify hospital adverse events. Studies in Health Technology and Informatics, 192, 1145–1145.

    Google Scholar 

  • Gkotsis, G., Velupillai, S., Oellrich, A., Dean, H., Liakata, M., & Dutta, R. (2016). Don’t let notes be misunderstood: A negation detection method for assessing risk of suicide in mental health records. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (pp. 95–105). Association for Computational Linguistics.

    Google Scholar 

  • Grigonyte, G., Kvist, M., Velupillai, S., & Wirén, M. Improving readability of Swedish electronic health records through lexical simplification: First results. In Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations – PITR, Gothenburg, Sweden, April 2014 (pp. 74–83). Association for Computational Linguistics. http://www.aclweb.org/anthology/W14-1209. Accessed 11 Jan 2018.

  • Gurulingappa, H., Rajput, A. M., Roberts, A., Fluck, J., Hofmann-Apitius, M., & Toldo, L. (2012). Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of Biomedical Informatics, 45(5), 885–892.

    CrossRef  Google Scholar 

  • Haerian, K., Salmasian, H., & Friedman, C. (2012). Methods for identifying suicide or suicidal ideation in EHRs. In AMIA Annual Symposium Proceedings (Vol. 2012, p. 1244). American Medical Informatics Association.

    Google Scholar 

  • Halpin, H., Shortell, S. M., Milstein, A., & Vanneman, M. (2011). Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs. American Journal of Infection Control, 39(4), 270–276.

    CrossRef  Google Scholar 

  • Harpaz, R., DuMouchel, W., Shah, N. H., Madigan, D., Ryan, P., & Friedman, C. (2012). Novel data-mining methodologies for adverse drug event discovery and analysis. Clinical Pharmacology & Therapeutics, 91(6), 1010–1021.

    CrossRef  Google Scholar 

  • Hassel, M. (2007). Resource Lean and Portable Automatic Text Summarization. PhD thesis, School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden, June 2007. http://nlp.lacasahassel.net/publications/hasselthesis07phd.pdf. Accessed 11 Jan 2018.

  • Hassel, M., & Sjöbergh, J. (2006). Towards holistic summarization: Selecting summaries, not sentences. In Proceedings of LREC 2006, Genoa, Italy. http://nlp.lacasahassel.net/publications/holsum06.pdf. Accessed 11 Jan 2018.

  • Hazlehurst, B., Naleway, A., & Mullooly, J. (2009). Detecting possible vaccine adverse events in clinical notes of the electronic medical record. Vaccine, 27(14), 2077–2083.

    CrossRef  Google Scholar 

  • Henriksson, A. (2015). Ensembles of Semantic Spaces, on Combining Models of Distributional Semantics with Applications in Healthcare. PhD thesis, Department of Computer and Systems Sciences, Stockholm University.

    Google Scholar 

  • Henriksson, A., & Hassel, M. (2013). Optimizing the dimensionality of clinical term spaces for improved diagnosis coding support. In Proceedings of Louhi Workshop on Health Document Text Mining and Information Analysis.

    Google Scholar 

  • Henriksson, A., Hassel, M., & Kvist, M. (2011). Diagnosis code assignment support using random indexing of patient records – A qualitative feasibility study. In Proceedings of Artificial Intelligence in Medicine (pp. 348–352). Berlin: Springer.

    CrossRef  Google Scholar 

  • Henriksson, A., Kvist, M., Dalianis, H., & Duneld, M. (2015). Identifying adverse drug event information in clinical notes with distributional semantic representations of context. Journal of Biomedical Informatics, 57, 333–349.

    CrossRef  Google Scholar 

  • High, R. (2012). The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works. IBM Corporation, Redbooks.

    Google Scholar 

  • Humphreys, H., & Smyth, E. T. M. (2006). Prevalence surveys of healthcare-associated infections: What do they tell us, if anything? Clinical Microbiology and Infection, 12(1), 2–4.

    CrossRef  Google Scholar 

  • Jacobson, O., & Dalianis, H. (2016). Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections. In ACL Proceedings of the 15th Workshop on Biomedical Natural Language Processing, BioNLP 2016 (pp. 191–195).

    Google Scholar 

  • Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395–405.

    CrossRef  Google Scholar 

  • Johnson, S. B., Bakken, S., Dine, D., Hyun, S., Mendonça, E., Morrison, F., et al. (2008). An electronic health record based on structured narrative. Journal of the American Medical Informatics Association, 15(1), 54–64.

    CrossRef  Google Scholar 

  • Kandula, S., Curtis, D., & Zeng-Treitler, Q. (2010). A semantic and syntactic text simplification tool for health content. In AMIA Annual Symposium Proceedings (Vol. 2010, pp. 366–370).

    Google Scholar 

  • Kanhov, M. (2014). Generating Descriptions for Concepts of Swedish SNOMED CT by Implementing a Natural Language Generation System. Master’s thesis, Department of Computer and Systems Sciences, Stockholm University.

    Google Scholar 

  • Kanhov, M., Feng, X., & Dalianis, H. (2012). Natural language generation from SNOMED specifications. In In the Proceedings of CLEF 2012 Workshop on Cross-Language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis (CLEFeHealth2012), Rome, September 17–18.

    Google Scholar 

  • Karimi, S., Metke-Jimenez, A., Kemp, M., & Wang, C. (2015a). Cadec: A corpus of adverse drug event annotations. Journal of Biomedical Informatics, 55, 73–81.

    CrossRef  Google Scholar 

  • Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., & Paris, C. (2015b). Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys (CSUR), 47(4), 56.

    CrossRef  Google Scholar 

  • Kavuluru, R., Rios, A., & Lu, Y. (2015). An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records. Artificial Intelligence in Medicine, 65(2), 155–166.

    CrossRef  Google Scholar 

  • Kokkinakis, D., Fors, K. L., Björkner, E., & Nordlund, A. (2017). Data collection from persons with mild forms of cognitive impairment and healthy controls-infrastructure for classification and prediction of dementia. In Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22–24 May 2017, Gothenburg, Sweden (pp. 172–182). Linköping University Electronic Press.

    Google Scholar 

  • Koopman, B., Karimi, S., Nguyen, A., McGuire, R., Muscatello, D., Kemp, M., et al. (2015a). Automatic classification of diseases from free-text death certificates for real-time surveillance. BMC Medical Informatics and Decision Making, 15(1), 53.

    Google Scholar 

  • Koopman, B., Zuccon, G., Wagholikar, A., Chu, K., O’Dwyer, J., Nguyen, A., et al. (2015b). Automated reconciliation of radiology reports and discharge summaries. In AMIA Annual Symposium Proceedings (Vol. 2015, pp. 775–784). American Medical Informatics Association.

    Google Scholar 

  • Korkontzelos, I., Mu, T., & Ananiadou, S. (2012). ASCOT: A text mining-based web-service for efficient search and assisted creation of clinical trials. BMC Medical Informatics and Decision Making, 12(1), S3.

    CrossRef  Google Scholar 

  • Lagos, K. (2016). Building an Artifact to Detect Adverse Drug Events in Stockholm EPR Corpus by Using the Stausberg and Hasford’s Framework. Master’s thesis, Department of Computer and Systems Sciences, Stockholm University.

    Google Scholar 

  • Läkemedelsverket. (2012). Läkemedelsboken 2011–2012, (In Swedish). Läkemedelsverket. https://lakemedelsboken.se/pdf/. Accessed 11 Jan 2018.

  • Lavergne, T., Névéol, A., Robert, A., Grouin, C., Rey, G., & Zweigenbaum, P. (2016). A dataset for ICD-10 coding of death certificates: Creation and usage. In the Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016), Held in Conjunction with Coling 2016 (pp. 60–69).

    Google Scholar 

  • Lee, D., Cornet, R., Lau, F., & De Keizer, N. (2013). A survey of SNOMED CT implementations. Journal of Biomedical Informatics, 46(1), 87–96.

    CrossRef  Google Scholar 

  • Lee, D., de Keizer, N., Lau, F., & Cornet, R. (2014). Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21(e1), e11–e19.

    CrossRef  Google Scholar 

  • Leonard Westgate, C., Shiner, B., Thompson, P., & Watts, B. V. (2015). Evaluation of veterans’ suicide risk with the use of linguistic detection methods. Psychiatric Services, 66(10), 1051–1056.

    CrossRef  Google Scholar 

  • Liu, S. (2009). Experiences with and reflections on text summarization tools. International Journal of Computational Intelligence Systems, 2(3), 202–218.

    Google Scholar 

  • Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159–165.

    CrossRef  MathSciNet  Google Scholar 

  • Luo, Y., Thompson, W. K., Herr, T. M., Zeng, Z., Berendsen, M. A., Jonnalagadda, S. R., et al. (2017). Natural language processing for EHR-based pharmacovigilance: A structured review. Drug Safety, 40(11), 1075–1089.

    CrossRef  Google Scholar 

  • Mani, I., & Maybury, M. T. (1999). Advances in Automatic Text Summarization (Vol. 293). Cambridge, MA: MIT Press.

    Google Scholar 

  • Martinez, D., & Li, Y. (2011). Information extraction from pathology reports in a hospital setting. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (pp. 1877–1882). New York: ACM.

    Google Scholar 

  • Metzger, M.-H., Durand, T., Lallich, S., Salamon, R., & Castets, P. (2012). The use of regional platforms for managing electronic health records for the production of regional public health indicators in France. BMC Medical Informatics and Decision Making, 12(1), 28.

    Google Scholar 

  • Metzger, M.-H., Tvardik, N., Gicquel, Q., Bouvry, C., Poulet, E., & Potinet-Pagliaroli, V. (2016). Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study. International Journal of Methods in Psychiatric Research, 26(2), 1–10.

    CrossRef  Google Scholar 

  • Moen, H. (2016). Distributional Semantic Models for Clinical Text Applied to Health Record Summarization. PhD thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, NTNU.

    Google Scholar 

  • Moen, H., Peltonen, L.-M., Heimonen, J., Airola, A., Pahikkala, T., Salakoski, T., et al. (2016). Comparison of automatic summarisation methods for clinical free text notes. Artificial Intelligence in Medicine, 67, 25–37.

    CrossRef  Google Scholar 

  • Napolitano, G., Marshall, A., Hamilton, P., & Gavin, A. T. (2016). Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction. Artificial Intelligence in Medicine, 70, 77–83.

    CrossRef  Google Scholar 

  • Nebeker, J. R., Barach, P., & Samore, M. H. (2004). Clarifying adverse drug events: A clinician’s guide to terminology, documentation, and reporting. Annals of Internal Medicine, 140(10), 795–801.

    CrossRef  Google Scholar 

  • Nguyen, A., Lawley, M., Hansen, D., & Colquist, S. (2011). Structured pathology reporting for cancer from free text: Lung cancer case study. Electronic Journal of Health Informatics, 7(1), 8.

    Google Scholar 

  • Nguyen, A. N., Moore, J., O’Dwyer, J., & Philpot, S. (2015). Assessing the utility of automatic cancer registry notifications data extraction from free-text pathology reports. In AMIA Annual Symposium Proceedings (Vol. 2015, p. 953). American Medical Informatics Association.

    Google Scholar 

  • Ou, Y., & Patrick, J. (2014). Automatic population of structured reports from narrative pathology reports. In Proceedings of the Seventh Australasian Workshop on Health Informatics and Knowledge Management (Vol. 153, pp. 41–50). Australian Computer Society, Inc.

    Google Scholar 

  • Pérez, A., Weegar, R., Casillas, A., Gojenola, K., Oronoz, M., & Dalianis, H. (2017). Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora. Journal of Biomedical Informatics, 71, 16–30.

    CrossRef  Google Scholar 

  • Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., & Elhadad, N. (2014). Diagnosis code assignment: Models and evaluation metrics. Journal of the American Medical Informatics Association, 21(2), 231–237.

    CrossRef  Google Scholar 

  • Pestian, J. P., Brew, C., Matykiewicz, P., Hovermale, D. J., Johnson, N., Cohen, K. B., et al. (2007). A shared task involving multi-label classification of clinical free text. In Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing (pp. 97–104). Association for Computational Linguistics.

    Google Scholar 

  • Pivovarov, R., & Elhadad, N. (2015). Automated methods for the summarization of electronic health records. Journal of the American Medical Informatics Association, 22(5), 938–947.

    CrossRef  Google Scholar 

  • Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., & Shneiderman, B. (1998). Lifelines: Using visualization to enhance navigation and analysis of patient records. In AMIA Annual Symposium Proceedings (pp. 76–80). American Medical Informatics Association.

    Google Scholar 

  • Portet, F., Reiter, E., Gatt, A., Hunter, J., Sripada, S., Freer, Y., et al. (2009). Automatic generation of textual summaries from neonatal intensive care data. Artificial Intelligence, 173(7–8), 789–816.

    CrossRef  Google Scholar 

  • Proux, D., Hagège, C., Gicquel, Q., Pereira, S., Darmoni, S., Segond, F., et al. (2011). Architecture and systems for monitoring hospital acquired infections inside a hospital information workflow. In Proceedings of the Workshop on Biomedical Natural Language Processing. USA: Portland, Oregon (p. 43e48). Citeseer.

    Google Scholar 

  • Ramesh, B. P., Houston, T. K., Brandt, C., Fang, H., & Yu, H. (2013). Improving patients’ electronic health record comprehension with NoteAid. In Medinfo (pp. 714–718).

    Google Scholar 

  • Roberts, K., Simpson, M., Demner-Fushman, D., Voorhees, E., & Hersh, W. (2016). State-of-the-art in biomedical literature retrieval for clinical cases: A survey of the TREC 2014 CDS track. Information Retrieval Journal, 19(1–2), 113–148.

    CrossRef  Google Scholar 

  • Roller, R., & Stevenson, M. (2014). Self-supervised relation extraction using UMLS. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 116–127). Berlin: Springer.

    Google Scholar 

  • Roque, F. S., Jensen, P. B., Schmock, H., Dalgaard, M., Andreatta, M., Hansen, T., et al. (2011a). Using electronic patient records to discover disease correlations and stratify patient cohorts. PLOS Computational Biology, 7(8), e1002141.

    CrossRef  Google Scholar 

  • Roque, F. S., Jensen, P. B., Schmock, H., Dalgaard, M., Andreatta, M., Hansen, T., et al. (2011b). Using electronic patient records to discover disease correlations and stratify patient cohorts. PLOS Computational Biology, 7(8), e1002141.

    CrossRef  Google Scholar 

  • SALAR. (2014). Swedish Association of Local Authorities and Regions: Vårdrelaterade infektioner framgångsfaktorer som förebygger. Stockholm, Sweden. http://webbutik.skl.se/bilder/artiklar/pdf/978-91-7585-109-9.pdf. Accessed 10 Apr 2014. ISBN 978-91-7585-109-9.

  • Santiso, S., Pérez, A., Gojenola, K., Taldea, I. X. A., Casillas, A., & Oronoz, M. (2014). Adverse drug event prediction combining shallow analysis and machine learning. In Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)@ EACL (pp. 85–89).

    Google Scholar 

  • Sarker, A., Mollá, D., & Paris, C. (2013). An approach for query-focused text summarisation for evidence based medicine. In Artificial Intelligence in Medicine (pp. 295–304). Berlin: Springer.

    CrossRef  Google Scholar 

  • Scharber, W. (2007). Evaluation of open source text mining tools for cancer surveillance. CDC, 24, 28. https://www.cdc.gov/cancer/npcr/pdf/aerro/text_mining_tools.pdf. Accessed 11 Jan 2018.

  • Singh, H., Knudsen Sollie, M., Orholm Solhøi, E., & Sverre Syberg, F. (2015). Information Extraction: The Case of Kreftregisteret, (In Norwegian). Bachelor’s thesis, Westerdals Oslo ACT.

    Google Scholar 

  • Skeppstedt, M., Kvist, M., Nilsson, G., & Dalianis, H. (2014). Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study. In Journal of Biomedical Informatics, 49, 148–158.

    CrossRef  Google Scholar 

  • Socialstyrelsen. (2010). The National Board of Health and Welfare, Kodningskvalitet i patientregistret, Slutenvård 2008, (In Swedish). http://www.socialstyrelsen.se/Lists/Artikelkatalog/Attachments/18082/2010-6-27.pdf.

  • Spasić, I., Livsey, J., Keane, J. A., & Nenadić, G. (2014). Text mining of cancer-related information: Review of current status and future directions. International Journal of Medical Informatics, 83(9), 605–623. http://dx.doi.org/10.1016/j.ijmedinf.2014.06.009. Accessed 11 Jan 2018.

    CrossRef  Google Scholar 

  • Stanfill, M. H., Williams, M., Fenton, S. H., Jenders, R. A., & Hersh, W. R. (2010). A systematic literature review of automated clinical coding and classification systems. Journal of the American Medical Informatics Association, 17(6), 646–651.

    CrossRef  Google Scholar 

  • Stausberg, J., & Hasford, J. (2011). Drug-related admissions and hospital-acquired adverse drug events in Germany: A longitudinal analysis from 2003 to 2007 of ICD-10-coded routine data. BMC Health Research, 11(1), 1.

    Google Scholar 

  • Suominen, H., Ginter, F., Pyysalo, S., Airola, A., Pahikkala, T., Salanterä, S., et al. (2008). Machine learning to automate the assignment of diagnosis codes to free-text radiology reports: A method description. In Proceedings of the ICML/UAI/COLT Workshop on Machine Learning for Health-Care Applications.

    Google Scholar 

  • Suominen, H., Salanterä, S., Velupillai, S., Chapman, W. W., Savova, G., Elhadad, N., et al. (2013). Overview of the ShARe/CLEF eHealth Evaluation Lab 2013. In Information Access Evaluation. Multilinguality, Multimodality, and Visualization (pp. 212–231). Berlin: Springer.

    Google Scholar 

  • Tang, R., Ouyang, L., Li, C., He, Y., Griffin, M., Taghian, A., et al. (2018). Machine learning to parse breast pathology reports in Chinese. Breast Cancer Research and Treatment, 1–8, https://doi.org/10.1007/s10549-018-4668-3.

  • Tanushi, H., Dalianis, H., & Nilsson, G. (2011). Calculating prevalence of comorbidity and comorbidity combinations with diabetes in hospital care in Sweden using a health care record database. In Proceedings of the LOUHI 2011, Third International Workshop on Health Document Text Mining and Information Analysis, Co-located with AIME 2011 Bled, Slovenia, July 6, 2011, CEUR-WS (Vol. 744, pp. 59–66). ISSN 1613-0073

    Google Scholar 

  • Tanushi, H., Kvist, M., & Sparrelid, E. (2014). Detection of healthcare-associated urinary tract infection in Swedish electronic health records. Studies in Health Technology and Informatics, 207, 330–339.

    Google Scholar 

  • Torgersson, O., & Falkman, G. (2002). Using text generation to access clinical data in a variety of contexts. Studies in Health Technology and Informatics, Vol 90, 460–465.

    MATH  Google Scholar 

  • Tran, T., Luo, W., Phung, D., Harvey, R., Berk, M., Kennedy, R. L., et al. (2014). Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments. BMC Psychiatry, 14(1), 76.

    CrossRef  Google Scholar 

  • Van Vleck, T. T., & Elhadad, N. (2010). Corpus-based problem selection for EHR note summarization. In AMIA Annual Symposium Proceedings (Vol. 2010, p. 817). American Medical Informatics Association.

    Google Scholar 

  • Voorhees, E. M., & Hersh, W. R. (2012). Overview of the TREC 2012 medical records track. In Proceedings of Text REtrieval Conference (TREC).

    Google Scholar 

  • Wang, X., Hripcsak, G., Markatou, M., & Friedman, C. (2009). Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: A feasibility study. Journal of the American Medical Informatics Association, 16(3), 328–337.

    CrossRef  Google Scholar 

  • Wang, Y., Coiera, E., Runciman, W., & Magrabi, F. (2017). Using multiclass classification to automate the identification of patient safety incident reports by type and severity. BMC Medical Informatics and Decision Making, 17(1), 84.

    CrossRef  Google Scholar 

  • Wang, Y., Patrick, J., Miller, G., & O’Hallaran, J. (2008). A computational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT. BMC Medical Informatics and Decision Making, 8(1), S5.

    CrossRef  Google Scholar 

  • Warrer, P., Hansen, E. H., Juhl-Jensen, L., & Aagaard, L. (2012). Using text-mining techniques in electronic patient records to identify ADRS from medicine use. British Journal of Clinical Pharmacology, 73(5), 674–684.

    CrossRef  Google Scholar 

  • Weegar, R., & Dalianis, H. (2015). Creating a rule based system for text mining of Norwegian breast cancer pathology reports. In Sixth International Workshop in Health Text Mining and Information Analysis (LOUHI), Held in Conjunction with EMNLP 2015, Lisbon, Portugal (pp. 73–78).

    Google Scholar 

  • Weegar, R., Kvist, M., Sundström, K., Brunak, S., & Dalianis, H. (2015). Finding cervical cancer symptoms in Swedish clinical text using a machine learning approach and NegEx. In AMIA Annual Symposium Proceedings (Vol. 2015, pp. 1296–1305). American Medical Informatics Association.

    Google Scholar 

  • Weegar, R., Nygård, J., & Dalianis, H. (2017). Efficient encoding of pathology reports using natural language processing. In Proceedings of Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria (pp. 778–783).

    Google Scholar 

  • Wester, K., Jönsson, A. K., Spigset, O., Druid, H., & Hägg, S. (2008). Incidence of fatal adverse drug reactions: A population based study. British Journal of Clinical Pharmacology, 65(4), 573–579.

    CrossRef  Google Scholar 

  • Yala, A., Barzilay, R., Salama, L., Griffin, M., Sollender, G., Bardia, A., et al. (2017). Using machine learning to parse breast pathology reports. Breast Cancer Research and Treatment, 161(2), 203–211.

    CrossRef  Google Scholar 

  • Zhao, D., & Weng, C. (2011). Combining PubMed knowledge and EHR data to develop a weighted Bayesian network for pancreatic cancer prediction. Journal of Biomedical Informatics, 44, 859–868.

    CrossRef  Google Scholar 

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  1. DSV-Stockholm University, Kista, Sweden

    Hercules Dalianis

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Dalianis, H. (2018). Applications of Clinical Text Mining. In: Clinical Text Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-78503-5_10

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