Using of Natural Language Processing Techniques in Suicide Research
Song J, Song TM, Seo D-C, Jin JH. Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents. Journal of Adolescent Health. 2016;59 (6):668-73.
Metzger MH, Tvardik N, Gicquel Q, Bouvry C, Poulet E, Potinet-Pagliaroli V. 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. 2016.
Wicentowski R, Sydes MR. Emotion Detection in Suicide Notes using Maximum Entropy Classification. Biomedical informatics insights. 2012;5(Suppl. 1):51-60.
Cook, Benjamin L., Ana M. Progovac, Pei Chen, Brian Mullin, Sherry Hou, and Enrique Baca-Garcia. "Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid." Computational and mathematical methods in medicine 2016 (2016).
McCart JA, Finch DK, Jarman J, Hickling E, Lind JD, Richardson MR, et al. Using ensemble models to classify the sentiment expressed in suicide notes. Biomedical informatics insights. 2012; 5(Suppl. 1):77-85.
Mościcki EK. Epidemiology of completed and attempted suicide: toward a framework for prevention. Clinical Neuroscience Research. 2001;1 (5):310-23.
Shekelle P, Bagley S, Munjas B. Strategies for Suicide Prevention in Veterans. 2009.
Taylor CL, van Ravesteyn LM, van denBerg MP, Stewart RJ, Howard LM. The prevalence and correlates of self-harm in pregnant women with psychotic disorder and bipolar disorder. Archives of women's mental health. 2016;19 (5):909-15.
Pestian JP, Matykiewicz P, Grupp-Phelan J, Lavanier SA, Combs J, Kowatch R. Using natural language processing to classify suicide notes. AMIA Annual Symposium proceedings AMIA Symposium. 2008:1091.
Quan C, Wang M, Ren F. An unsupervised text mining method for relation extraction from biomedical literature. PloS one. 2014;9 (7):e102039.
Organization WH. Preventing suicide: a global imperative: World Health Organization; 2014.
Olive J, Christianson C, McCary J. Handbook of natural language processing and machine translation: DARPA global autonomous language exploitation: Springer Science & Business Media; 2011.
Tang B, Chen Q, Wang X, Wu Y, Zhang Y, Jiang M, et al., editors. Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods. AMIA Annual Symposium Proceedings; 2015: American Medical Informatics Association.
Pestian J, Nasrallah H, Matykiewicz P, Bennett A, Leenaars A. Suicide Note Classification Using Natural Language Processing: A Content Analysis. Biomedical informatics insights. 2010;2010 (3):19-28.
Yang H, Willis A, de Roeck A, Nuseibeh B. A hybrid model for automatic emotion recognition in suicide notes. Biomedical informatics insights. 2012;5 (Suppl. 1):17-30.
Pestian JP, Matykiewicz P, Linn-Gust M, South B, Uzuner O, Wiebe J, et al. Sentiment analysis of suicide notes: A shared task. Biomedical informatics insights. 2012;5 (Suppl. 1):3.
Desmet B, Hoste V. Emotion detection in suicide notes. Expert Systems with Applications. 2013;40 (16):6351-8.
Desmet B, Hoste V. Combining Lexico-semantic Features for Emotion Classification in Suicide Notes. Biomedical informatics insights. 2012;5 (Suppl. 1):125-8.
Sohn S, Torii M, Li D, Wagholikar K, Wu S, Liu H. A hybrid approach to sentiment sentence classification in suicide notes. Biomedical informatics insights. 2012;5 (Suppl. 1):43-50.
Yeh E, Jarrold W, Jordan J. Leveraging psycholinguistic resources and emotional sequence models for suicide note emotion annotation. Biomedical informatics insights. 2012;5 (Suppl. 1):155-63.
Spasic I, Burnap P, Greenwood M, Arribas-Ayllon M. A naive bayes approach to classifying topics in suicide notes. Biomedical informatics insights. 2012;5(Suppl. 1):87-97.
Kovacevic A, Dehghan A, Keane JA, Nenadic G. Topic categorisation of statements in suicide notes with integrated rules and machine learning. Biomedical informatics insights. 2012;5 (Suppl. 1):115-24.
Cherry C, Mohammad SM, de Bruijn B. Binary classifiers and latent sequence models for emotion detection in suicide notes. Biomedical informatics insights. 2012;5 (Suppl. 1):147-54.
Anderson HD, Pace WD, Brandt E, Nielsen RD, Allen RR, Libby AM, et al. Monitoring suicidal patients in primary care using electronic health records. Journal of the American Board of Family Medicine: JABFM. 2015;28 (1):65-71.
Karmen C, Hsiung RC, Wetter T. Screening Internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods. Computer methods and programs in biomedicine. 2015;120 (1):27-36.
Woo H, Cho Y, Shim E, Lee K, Song G. Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood. International journal of environmental research and public health. 2015;12 (9):10974-83.
McCoy TH, Jr., Castro VM, Roberson AM, Snapper LA, Perlis RH. Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing. JAMA psychiatry. 2016;73 (10):1064-71.
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