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Mining Free-Text Medical Notes for Suicide Risk Assessment

Published: 09 July 2018 Publication History

Abstract

Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

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      cover image ACM Other conferences
      SETN '18: Proceedings of the 10th Hellenic Conference on Artificial Intelligence
      July 2018
      339 pages
      ISBN:9781450364331
      DOI:10.1145/3200947
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      • EETN: Hellenic Artificial Intelligence Society
      • UOP: University of Patras
      • University of Thessaly: University of Thessaly, Volos, Greece

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 09 July 2018

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      Author Tags

      1. automated machine learning
      2. clinical data
      3. risk assessment tool
      4. suicide prevention
      5. text mining

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      • (2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
      • (2024)Socio-technical Imaginaries: Envisioning and Understanding AI Parenting Supports through Design FictionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642619(1-27)Online publication date: 11-May-2024
      • (2024)Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRsnpj Digital Medicine10.1038/s41746-024-01266-77:1Online publication date: 28-Sep-2024
      • (2024)Decoding depression: Analyzing social network insights for depression severity assessment with transformers and explainable AINatural Language Processing Journal10.1016/j.nlp.2024.1000797(100079)Online publication date: Jun-2024
      • (2024)Mood-Based Prioritization Model in People with Suicidal Tendencies Using TopsisIntegrated Science for Sustainable Development Goal 310.1007/978-3-031-64288-3_7(133-152)Online publication date: 12-Dec-2024
      • (2023)Designing Human-centered AI for Mental Health: Developing Clinically Relevant Applications for Online CBT TreatmentACM Transactions on Computer-Human Interaction10.1145/356475230:2(1-50)Online publication date: 17-Mar-2023
      • (2023)A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learningIBRO Neuroscience Reports10.1016/j.ibneur.2023.06.00815(77-89)Online publication date: Dec-2023
      • (2022)Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research DirectionsElectronics10.3390/electronics1107111111:7(1111)Online publication date: 31-Mar-2022
      • (2022)Natural language processing applied to mental illness detection: a narrative reviewnpj Digital Medicine10.1038/s41746-022-00589-75:1Online publication date: 8-Apr-2022
      • (2022)Just Add Data: automated predictive modeling for knowledge discovery and feature selectionnpj Precision Oncology10.1038/s41698-022-00274-86:1Online publication date: 16-Jun-2022
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