Abstract
Mental health is an increasingly important problem in healthcare. Psychiatric stressors are one of the major contributors of mental disorders. Very few studies have investigated stressor data in electronic health records, mostly because they are recorded in narrative texts. This study takes the initiative to develop a natural language processing system to automatically extract psychiatric stressors from clinical notes. Our approach integrates domain knowledge from multiple sources and unsupervised word representation features generated from deep learning based algorithms, to address the context dependence and data sparseness challenges caused by idiosyncratic psychosocial backgrounds. Experimental results on psychiatric notes from the CEGS N-GRID 2016 challenge demonstrate that the proposed approach is promising. The best performing configuration achieved a precision of 90.5%, a recall of 65.5%, and a F-measure of 76.0% for inexact matching.
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References
Aronson, A.R., Lang, F.M.: An overview of MetaMap: historical perspective and recent advances. J. Am. Med. Inf. Assoc. 17(3), 229–236 (2010)
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl 1), D267–D270 (2004)
Cho, H.C., Okazaki, N., Miwa, M., Tsujii, J.: Named entity recognition with multiple segment representations. Inf. Process. Manag. 49(4), 954–965 (2013)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: The 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)
Friedman, M.J., Resick, P.A., Bryant, R.A., Strain, J., Horowitz, M., Spiegel, D.: Classification of trauma and stressor-related disorders in DSM-5. Depression and anxiety 28(9), 737–749 (2011). http://onlinelibrary.wiley.com/doi/10.1002/da.20845/full
Gorrell, G., Jackson, R., Roberts, A., Stewart, R.: Finding negative symptoms of schizophrenia in patient records. In: Proceedings of the NLP Med Biol Work (NLPMedBio), Recent Adv Nat Lang Process (RANLP), pp. 9–17 (2013)
Guo, J., Che, W., Wang, H., Liu, T.: Revisiting embedding features for simple semi-supervised learning. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 110–120 (2014)
Insel, T.R.: The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am. J. Psychiatry (2014). http://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2014.14020138
Kazama, J., Makino, T., Ohta, Y., Tsujii, J.: Tuning support vector machines for biomedical named entity recognition. In: Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain, vol. 3, pp. 1–8. Association for Computational Linguistics (2002)
Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)
Leaman, R., Doğan, R.I., Lu, Z.: DNorm: disease name normalization with pairwise learning to rank. Bioinformatics, p. btt474 (2013)
Leaman, R., Lu, Z.: TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics 32(18), 2839–2846 (2016)
McCoy, T.H., Castro, V.M., Roberson, A.M., Snapper, L.A., Perlis, R.H.: Improving prediction of suicide and accidental death after discharge from general hospitals with natural language processing. Jama Psychiatry 73(10), 1064–1071 (2016)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)
Nadeau, D., Turney, P.D., Matwin, S.: Unsupervised named-entity recognition: generating gazetteers and resolving ambiguity. In: Lamontagne, L., Marchand, M. (eds.) AI 2006. LNCS, vol. 4013, pp. 266–277. Springer, Heidelberg (2006). doi:10.1007/11766247_23
Organization, W.H., et al.: Prevention of mental disorders: effective interventions and policy options: Summary report (2004). http://apps.who.int/iris/handle/10665/43027
Patel, R., Wilson, R., Jackson, R., Ball, M., Shetty, H., Broadbent, M., Stewart, R., McGuire, P., Bhattacharyya, S.: Cannabis use and treatment resistance in first episode psychosis: a natural language processing study. Lancet 385, S79 (2015)
Pestian, J.P., Grupp-Phelan, J., Bretonnel Cohen, K., Meyers, G., Richey, L.A., Matykiewicz, P., Sorter, M.T.: A controlled trial using natural language processing to examine the language of suicidal adolescents in the emergency department. Suicide and Life-threatening Behavior (2015)
Proctor, E.K., Landsverk, J., Aarons, G., Chambers, D., Glisson, C., Mittman, B.: Implementation research in mental health services: an emerging science with conceptual, methodological, and training challenges. Adm. Policy Ment. Health Ment. Health Serv. Res. 36(1), 24–34 (2009)
Rumshisky, A., Ghassemi, M., Naumann, T., Szolovits, P., Castro, V.M., McCoy, T.H., Perlis, R.H.: Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Transl. Psychiatry 6(10), e921 (2016)
Saeed, M., Villarroel, M., Reisner, A.T., Clifford, G., Lehman, L.W., Moody, G., Heldt, T., Kyaw, T.H., Moody, B., Mark, R.G.: Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database. Crit. Care Med. 39(5), 952 (2011)
Tang, B., Feng, Y., Wang, X., Wu, Y., Zhang, Y., Jiang, M., Wang, J., Xu, H.: A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature. J. Cheminform. 7(supplement 1), S8 (2015)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Language Processing, pp. 347–354. Association for Computational Linguistics (2005)
Zhang, Y., Wang, J., Tang, B., Wu, Y., Jiang, M., Chen, Y., Xu, H.: UTH_CCB: A Report for SemEval 2014 Task 7 Analysis of Clinical Text. SemEval 2014, pp. 802–806 (2014)
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We thank the organizers of the CEGS N-GRID 2016 challenge for providing the corpus.
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Zhang, O.R., Zhang, Y., Xu, J., Roberts, K., Zhang, X.Y., Xu, H. (2017). Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_41
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DOI: https://doi.org/10.1007/978-3-319-60045-1_41
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