Detección de Indicios de Autolesiones No Suicidas en Informes Médicos de Psiquiatría Mediante el Análisis del Lenguaje

  1. Martínez Romo, Juan
  2. Araujo Serna, Lourdes
  3. Reneses, Blanca
  4. Sevilla-Llewellyn-Jones, Julia
  5. Martínez-Capella, Ignacio
  6. Seara-Aguilar, Germán
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Ano de publicación: 2022

Número: 69

Páxinas: 129-140

Tipo: Artigo

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Outras publicacións en: Procesamiento del lenguaje natural

Resumo

Non-suicidal self-injury, often referred to as self-injury, is the act of deliberately harming one’s own body, such as cutting or burning oneself. It is not usually intended as a suicide attempt. This paper presents a system for detecting signs of non-suicidal self-injury, based on language analysis, on an annotated set of medical reports obtained from the psychiatric service of a public hospital in Madrid. Both explainability and accuracy in predicting positive cases are the two main objectives of this work. In order to achieve this goal, two supervised systems of different natures have been developed. On the one hand, a process of extraction of different features focused on the world of self-injury itself has been carried out using natural language processing techniques to subsequently feed a traditional classifier. On the other hand, a deep learning system based on several layers of convolutional neural networks, due to its high performance in text classification tasks. The result are two supervised systems with high performance, where we highlight the system based on a traditional classifier due to its better prediction of positive classes and the greater ease to explain its results to health professionals.

Referencias bibliográficas

  • Ageitos, E. C., J. Martínez-Romo, y L. Araujo. 2020. Nlp-uned at erisk 2020: Selfharm early risk detection with sentiment analysis and linguistic features. En CLEF (Working Notes).
  • Baetens, I., L. Claes, J. Muehlenkamp, H. Grietens, y P. Onghena. 2011. Non- Suicidal and Suicidal Self-Injurious Behavior among Flemish Adolescents: A Web- Survey. Archives of Suicide Research, 15(1):56–67.
  • Burke, T. A., B. A. Ammerman, y R. Jacobucci. 2019. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of affective di- sorders, 245:869–884.
  • Campillo-Ageitos, E., H. Fabregat, L. Araujo, y J. Martinez-Romo. 2021. Nlp-uned at erisk 2021: self-harm early risk detection with tf-idf and linguistic features. Working Notes of CLEF, páginas 21–24.
  • Delgado-Gomez, D., E. Baca-Garcia, D. Aguado, P. Courtet, y J. Lopez- Castroman. 2016. Computerized adaptive test vs. decision trees: development of a support decision system to identify suicidal behavior. Journal of affective disorders, 206:204–209.
  • Devlin, J., M.-W. Chang, K. Lee, y K. Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Fabregat, H., L. Araujo Serna, y J. Martínez Romo. 2019. Deep learning approach for negation trigger and scope recognition.
  • Fabregat, H., A. Duque, J. Martínez-Romo, y L. Araujo. 2019. Extending a deep learning approach for negation cues detection in spanish. En IberLEF@ SEPLN, páginas 369–377.
  • Fernandes, A. C., R. Dutta, S. Velupillai, J. Sanyal, R. Stewart, y D. Chandran. 2018. Identifying suicide ideation and suicidal attempts in a psychiatric clinical research database using natural language processing. Scientific reports, 8(1):1–10.
  • Greaves, M. M. 2018a. A Corpus Linguistic Analysis of Public Reddit and Tumblr Blog Posts on Non-Suicidal Self-Injury, An abstract. Ph.D. tesis, College of Education, Oregon State University.
  • Greaves, M. M. 2018b. A corpus linguistic analysis of public reddit and tumblr blog posts on non-suicidal self-injury.
  • Haerian, K., H. Salmasian, y C. Friedman. 2012. Methods for identifying suicide or suicidal ideation in ehrs. En AMIA annual symposium proceedings, volumen 2012, página 1244. American Medical Informatics Association.
  • Kessler, R. C., M. B. Stein, M. V. Petukhova, P. Bliese, R. M. Bossarte, E. J. Bromet, C. S. Fullerton, S. E. Gilman, C. Ivany,
  • L. Lewandowski-Romps, y others. 2017. Predicting suicides after outpatient mental health visits in the army study to assess risk and resilience in servicemembers (army starrs). Molecular psychiatry, 22(4):544–551.
  • LeCun, Y., Y. Bengio, y G. Hinton. 2015. Deep learning. nature, 521(7553):436– 444.
  • Losada, D. E., F. Crestani, y J. Parapar. 2019. Overview of erisk 2019 early risk prediction on the internet. En International Conference of the Cross-Language Evaluation Forum for European Languages, páginas 340–357. Springer.
  • Losada, D. E., F. Crestani, y J. Parapar. 2020. erisk 2020: Self-harm and depression challenges. En European Conference on Information Retrieval, páginas 557– 563. Springer.
  • Loyola, J. M., S. Burdisso, H. Thompson, L. Cagnina, y M. Errecalde. 2021. Unsl at erisk 2021: A comparison of three early alert policies for early risk detection. En Working Notes of CLEF 2021-Conference and Labs of the Evaluation Forum, Bucarest, Romania.
  • Mann, J. J., S. P. Ellis, C. M. Waternaux, X. Liu, M. A. Oquendo, K. M. Malone, B. S. Brodsky, G. L. Haas, y D. Currier. 2008. Classification trees distinguish suicide attempters in major psychiatric disorders: a model of clinical decision making. The Journal of clinical psychiatry, 69(1):2693.
  • Martınez-Castano, R., A. Htait, L. Azzopardi, y Y.Moshfeghi. 2020. Early risk detection of self-harm and depression severity using bert-based transformers. Working Notes of CLEF, página 16.
  • McCoy, T. H., V. M. Castro, A. M. Roberson, L. A. Snapper, y R. H. Perlis. 2016. Improving prediction of suicide and accidental death after discharge from general hospitals with natural language processing. JAMA psychiatry, 73(10):1064–1071.
  • Metzger, M.-H., N. Tvardik, Q. Gicquel, C. Bouvry, E. Poulet, y V. Potinet- Pagliaroli. 2017. Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a french pilot study. In- ternational journal of methods in psychiatric research, 26(2):e1522.
  • Nicolai, K. A., M. D. Wielgus, y A. Mezulis. 2016. Identifying Risk for Self-Harm: Rumination and Negative Affectivity in the Prospective Prediction of Nonsuicidal Self-Injury. Suicide and Life-Threatening Behavior, 46(2):223–233.
  • Obeid, J. S., J. Dahne, S. Christensen, S. Howard, T. Crawford, L. J. Frey, T. Stecker, y B. E. Bunnell. 2020. Identifying and predicting intentional self-harm in electronic health record clinical notes: deep learning approach. JMIR medical informatics, 8(7):e17784.
  • Obeid, J. S., E. R.Weeda, A. J. Matuskowitz, K. Gagnon, T. Crawford, C. M. Carr, y L. J. Frey. 2019. Automated detection of altered mental status in emergency department clinical notes: a deep learning approach. BMC medical informatics and decision making, 19(1):1–9.
  • Parapar, J., P. Martín-Rodilla, D. E. Losada, y F. Crestani. 2021. Overview of erisk 2021: Early risk prediction on the internet. En International Conference of the Cross- Language Evaluation Forum for European Languages, páginas 324–344. Springer.
  • Pennebaker, J. W., M. R. Mehl, y K. G. Niederhoffer. 2003. Psychological aspects of natural language use: Our words, our selves. Annual review of psychology, 54(1):547–577.
  • Poulin, C., B. Shiner, P. Thompson, L. Vepstas, Y. Young-Xu, B. Goertzel, B. Watts, L. Flashman, y T. McAllister. 2014. Predicting the risk of suicide by analyzing the text of clinical notes. PloS one, 9(1):e85733.
  • Rodham, K., K. Hawton, y E. Evans. 2004. Reasons for deliberate self-harm: Comparison of self-poisoners and self-cutters in a community sample of adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 43(1):80–87.
  • Rozova, V., K. Witt, J. Robinson, Y. Li, y K. Verspoor. 2022. Detection of selfharm and suicidal ideation in emergency department triage notes. Journal of the American Medical Informatics Association, 29(3):472–480.
  • Walsh, C. G., J. D. Ribeiro, y J. C. Franklin. 2017. Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 5(3):457–469. Young, R., M. Van Beinum, H. Sweeting, y P. West. 2007. Young people who selfharm. The British Journal of Psychiatry, 191(1):44–49.
Fonte de datos: Dialnet