{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T20:23:39Z","timestamp":1775852619600,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Biomed Semant"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Up to 35% of nurses\u2019 working time is spent on care documentation. We describe the evaluation of a system aimed at assisting nurses in documenting patient care and potentially reducing the documentation workload. Our goal is to enable nurses to write or dictate nursing notes in a narrative manner without having to manually structure their text under subject headings. In the current care classification standard used in the targeted hospital, there are more than 500 subject headings to choose from, making it challenging and time consuming for nurses to use.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>The task of the presented system is to automatically group sentences into paragraphs and assign subject headings. For classification the system relies on a neural network-based text classification model. The nursing notes are initially classified on sentence level. Subsequently coherent paragraphs are constructed from related sentences.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>Based on a manual evaluation conducted by a group of three domain experts, we find that in about 69% of the paragraphs formed by the system the topics of the sentences are coherent and the assigned paragraph headings correctly describe the topics. We also show that the use of a paragraph merging step reduces the number of paragraphs produced by 23% without affecting the performance of the system.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The study shows that the presented system produces a coherent and logical structure for freely written nursing narratives and has the potential to reduce the time and effort nurses are currently spending on documenting care in hospitals.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s13326-020-00229-7","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T12:03:43Z","timestamp":1598961823000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings"],"prefix":"10.1186","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1418-7892","authenticated-orcid":false,"given":"Hans","family":"Moen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Hakala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura-Maria","family":"Peltonen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanna-Maria","family":"Matinolli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"Suhonen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirsi","family":"Terho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riitta","family":"Danielsson-Ojala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maija","family":"Valta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Filip","family":"Ginter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tapio","family":"Salakoski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanna","family":"Salanter\u00e4","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"issue":"6","key":"229_CR1","first-page":"287","volume":"30","author":"T Yee","year":"2012","unstructured":"Yee T, Needleman J, Pearson M, Parkerton P, Parkerton M, Wolstein J. The influence of integrated electronic medical records and computerized nursing notes on nurses\u2019 time spent in documentation. Comput Inform Nurs. 2012; 30(6):287\u201392.","journal-title":"Comput Inform Nurs"},{"issue":"4","key":"229_CR2","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1111\/scs.12094","volume":"28","author":"K Saranto","year":"2014","unstructured":"Saranto K, Kinnunen U-M, Kivek\u00e4s E, Lappalainen A-M, Liljamo P, Rajalahti E, Hypp\u00f6nen H. Impacts of structuring nursing records: a systematic review. Scand J Caring Sci. 2014; 28(4):629\u201347. https:\/\/doi.org\/10.1111\/scs.12094.","journal-title":"Scand J Caring Sci"},{"issue":"3","key":"229_CR3","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.ijmedinf.2013.11.006","volume":"83","author":"H Hypp\u00f6nen","year":"2014","unstructured":"Hypp\u00f6nen H, Saranto K, Vuokko R, M\u00e4kel\u00e4-Bengs P, Doupi P, Lindqvist M, M\u00e4kel\u00e4 M. Impacts of structuring the electronic health record: A systematic review protocol and results of previous reviews. Int J Med Inform. 2014; 83(3):159\u201369. https:\/\/doi.org\/10.1016\/j.ijmedinf.2013.11.006.","journal-title":"Int J Med Inform"},{"key":"229_CR4","first-page":"776","volume":"146","author":"P Hoffr\u00e9n","year":"2008","unstructured":"Hoffr\u00e9n P, Leivonen K, Miettinen M. Nursing standardized documentation in Kuopio University Hospital. Stud Health Technol Inform. 2008; 146:776\u20137.","journal-title":"Stud Health Technol Inform"},{"issue":"8","key":"229_CR5","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.ijmedinf.2010.05.002","volume":"79","author":"K H\u00e4yrinen","year":"2010","unstructured":"H\u00e4yrinen K, Lammintakanen J, Saranto K. Evaluation of electronic nursing documentation \u2013 Nursing process model and standardized terminologies as keys to visible and transparent nursing. Int J Med Inform. 2010; 79(8):554\u201364.","journal-title":"Int J Med Inform"},{"key":"229_CR6","volume-title":"Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis","author":"H Moen","year":"2018","unstructured":"Moen H, Hakala K, Peltonen L-M, Suhonen H, Loukasm\u00e4ki P, Salakoski T, Ginter F, Salanter\u00e4 S. Evaluation of a prototype system that automatically assigns subject headings to nursing narratives using recurrent neural network. In: Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis. Brussels, Belgium: Association for Computational Linguistics: 2018. p. 94\u2013100."},{"issue":"8","key":"229_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9(8):1735\u201380.","journal-title":"Neural Comput"},{"issue":"10","key":"229_CR8","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F. Learning to forget: Continual prediction with LSTM. Neural Comput. 2000; 12(10):2451\u201371.","journal-title":"Neural Comput"},{"key":"229_CR9","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"J Zeng","year":"2018","unstructured":"Zeng J, Li J, Song Y, Gao C, Lyu MR, King I. Topic memory networks for short text classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics: 2018. p. 3120\u201331."},{"key":"229_CR10","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 2018."},{"key":"229_CR11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1441","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"X Liu","year":"2019","unstructured":"Liu X, He P, Chen W, Gao J. Multi-task deep neural networks for natural language understanding. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics: 2019. p. 4487\u201396. https:\/\/doi.org\/10.18653\/v1\/P19-1441."},{"key":"229_CR12","volume-title":"Advances in Neural Information Processing Systems 28","author":"X Zhang","year":"2015","unstructured":"Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, editors. Advances in Neural Information Processing Systems 28. Red Hook, NY, USA: Curran Associates, Inc.: 2015. p. 649\u201357."},{"key":"229_CR13","doi-asserted-by":"crossref","unstructured":"Larkey LS, Croft WB. Combining classifiers in text categorization. In: SIGIR. Citeseer: 1996. p. 289\u201397.","DOI":"10.1145\/243199.243276"},{"key":"229_CR14","doi-asserted-by":"crossref","unstructured":"Pestian JP, Brew C, Matykiewicz P, Hovermale DJ, Johnson N, Cohen KB, Duch W. 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. Association for Computational Linguistics: 2007. p. 97\u2013104.","DOI":"10.3115\/1572392.1572411"},{"issue":"2","key":"229_CR15","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.artmed.2015.04.007","volume":"65","author":"R Kavuluru","year":"2015","unstructured":"Kavuluru R, Rios A, Lu Y. An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records. Artif Intell Med. 2015; 65(2):155\u201366.","journal-title":"Artif Intell Med"},{"key":"229_CR16","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"P Xie","year":"2018","unstructured":"Xie P, Xing E. A neural architecture for automated ICD coding. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics: 2018. p. 1066\u201376."},{"issue":"11","key":"229_CR17","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1016\/j.ijmedinf.2015.08.004","volume":"84","author":"B Koopman","year":"2015","unstructured":"Koopman B, Zuccon G, Nguyen A, Bergheim A, Grayson N. Automatic ICD-10 classification of cancers from free-text death certificates. Int J Med Inform. 2015; 84(11):956\u201365.","journal-title":"Int J Med Inform"},{"issue":"1","key":"229_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-018-0723-6","volume":"19","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sohn S, Liu S, Shen F, Wang L, Atkinson EJ, Amin S, Liu H. A clinical text classification paradigm using weak supervision and deep representation. BMC Med Inform Decision Making. 2019; 19(1):1.","journal-title":"BMC Med Inform Decision Making"},{"key":"229_CR19","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.eswa.2018.09.034","volume":"116","author":"G Mujtaba","year":"2019","unstructured":"Mujtaba G, Shuib L, Idris N, Hoo WL, Raj RG, Khowaja K, Shaikh K, Nweke HF. Clinical text classification research trends: systematic literature review and open issues. Expert Syst Appl. 2019; 116:494\u2013520.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"229_CR20","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1186\/s12911-019-0894-9","volume":"19","author":"JS Obeid","year":"2019","unstructured":"Obeid JS, Weeda ER, Matuskowitz AJ, Gagnon K, Crawford T, Carr CM, Frey LJ. Automated detection of altered mental status in emergency department clinical notes: a deep learning approach. BMC Med Inform Decision Making. 2019; 19(1):164.","journal-title":"BMC Med Inform Decision Making"},{"issue":"12","key":"229_CR21","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1093\/jamia\/ocz164","volume":"26","author":"L Yao","year":"2019","unstructured":"Yao L, Jin Z, Mao C, Zhang Y, Luo Y. Traditional Chinese medicine clinical records classification with BERT and domain specific corpora. J Am Med Inform Assoc. 2019; 26(12):1632\u20136. https:\/\/doi.org\/10.1093\/jamia\/ocz164.","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"229_CR22","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/fi11120255","volume":"11","author":"L Qing","year":"2019","unstructured":"Qing L, Linhong W, Xuehai D. A novel neural network-based method for medical text classification. Futur Internet. 2019; 11(12):255.","journal-title":"Futur Internet"},{"issue":"6","key":"229_CR23","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1197\/jamia.M3037","volume":"16","author":"JC Denny","year":"2009","unstructured":"Denny JC, Spickard III A, Johnson KB, Peterson NB, Peterson JF, Miller RA. Evaluation of a method to identify and categorize section headers in clinical documents. J Am Med Inform Assoc. 2009; 16(6):806\u201315.","journal-title":"J Am Med Inform Assoc"},{"key":"229_CR24","volume-title":"Proceedings of the 1st ACM International Health Informatics Symposium IHI \u201910","author":"Y Li","year":"2010","unstructured":"Li Y, Lipsky Gorman S, Elhadad N. Section classification in clinical notes using supervised Hidden Markov Model. In: Proceedings of the 1st ACM International Health Informatics Symposium IHI \u201910. New York, NY, USA: ACM: 2010. p. 744\u201350."},{"key":"229_CR25","unstructured":"Haug PJ, Wu X, Ferraro JP, Savova GK, Huff SM, Chute CG. Developing a section labeler for clinical documents. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association: 2014. p. 636."},{"key":"229_CR26","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 2013."},{"key":"229_CR27","unstructured":"Kingma D, Ba J. ADAM: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014."},{"key":"229_CR28","unstructured":"Chollet F, et al. Keras. 2015. https:\/\/keras.io. Accessed 01 Jan 2020."},{"key":"229_CR29","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Man\u00e9 D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Vi\u00e9gas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X. TensorFlow: Large-scale machine learning on heterogeneous systems. 2015. Software available from tensorflow.org."},{"issue":"1","key":"229_CR30","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1093\/jamia\/ocz150","volume":"27","author":"H Moen","year":"2019","unstructured":"Moen H, Hakala K, Peltonen L-M, Suhonen H, Ginter F, Salakoski T, Salanter\u00e4 S. Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods. J Am Med Inform Assoc. 2019; 27(1):81\u20138. https:\/\/doi.org\/10.1093\/jamia\/ocz150.","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"229_CR31","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998; 86(11):2278\u2013324.","journal-title":"Proc IEEE"},{"key":"229_CR32","volume-title":"Advances in Kernel Methods \u2013 Support Vector Learning","author":"T Joachims","year":"1999","unstructured":"Joachims T. Making large-scale SVM learning practical In: Sch\u00f6lkopf B, Burges C, Smola A, editors. Advances in Kernel Methods \u2013 Support Vector Learning. Cambridge, MA: MIT Press: 1999. p. 169\u201384. Chap. 11."},{"issue":"3","key":"229_CR33","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M, et al. Classification and regression by RandomForest. R news. 2002; 2(3):18\u201322.","journal-title":"R news"},{"key":"229_CR34","volume-title":"Natural Language Processing with Python","author":"S Bird","year":"2009","unstructured":"Bird S, Loper E, Klein E. Natural Language Processing with Python. Sebastopol, California, USA: O\u2019Reilly Media Inc.; 2009."},{"key":"229_CR35","unstructured":"McInnes BT, Pedersen T, Pakhomov SV. UMLS-Interface and UMLS-Similarity: open source software for measuring paths and semantic similarity. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association: 2009. p. 431."},{"issue":"7\/8","key":"229_CR36","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1097\/NNA.0b013e3181ae94f8","volume":"39","author":"J Carter-Wesley","year":"2009","unstructured":"Carter-Wesley J. Voice recognition dictation for nurses. J Nurs Adm. 2009; 39(7\/8):310\u20132.","journal-title":"J Nurs Adm"},{"issue":"4","key":"229_CR37","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1093\/jamia\/ocy179","volume":"26","author":"SV Blackley","year":"2019","unstructured":"Blackley SV, Huynh J, Wang L, Korach Z, Zhou L. Speech recognition for clinical documentation from 1990 to 2018: A systematic review. J Am Med Inform Assoc. 2019; 26(4):324\u201338.","journal-title":"J Am Med Inform Assoc"},{"key":"229_CR38","unstructured":"Liljamo P, Kinnunen U-M, Ensio A. FinCC-luokituskokonaisuuden k\u00e4ytt\u00f6opas-SHTaL 3.0, SHToL 3.0, SHTuL 1.0: THL; 2012."},{"key":"229_CR39","volume-title":"Clinical Care Classification (CCC) System Manual: a Guide to Nursing Documentation","author":"V Saba","year":"2006","unstructured":"Saba V. Clinical Care Classification (CCC) System Manual: a Guide to Nursing Documentation. New York, NY, USA: Springer; 2006."}],"container-title":["Journal of Biomedical Semantics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-020-00229-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13326-020-00229-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-020-00229-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:47:54Z","timestamp":1630457274000},"score":1,"resource":{"primary":{"URL":"https:\/\/jbiomedsem.biomedcentral.com\/articles\/10.1186\/s13326-020-00229-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,1]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["229"],"URL":"https:\/\/doi.org\/10.1186\/s13326-020-00229-7","relation":{},"ISSN":["2041-1480"],"issn-type":[{"value":"2041-1480","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,1]]},"assertion":[{"value":"19 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Ethical approval for using the data was obtained from the hospital district\u2019s ethics committee (17.02.2009 \u00a767) and research approval was obtained from the medical director of the hospital district (02\/2009).","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"10"}}