A computational model for assisting individuals with suicidal ideation based on context histories
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10209-023-00991-2.pdf
Reference59 articles.
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2. Ansari, L., Ji, S., Chen, Q., et al.: Ensemble hybrid learning methods for automated depression detection. In: IEEE Transactions on Computational Social Systems, pp. 1–9 (2022). https://doi.org/10.1109/TCSS.2022.3154442
3. Aranda, J.A.S., Dias, L.P.S., Barbosa, J.L.V., et al.: Collection and analysis of physiological data in smart environments: a systematic mapping. J. Ambient Intell. Humaniz. Comput. 11(7), 2883–2897 (2020). https://doi.org/10.1007/s12652-019-01409-9
4. Aranda, J.A.S., Bavaresco, R.S., de Carvalho, J.V., et al.: A computational model for adaptive recording of vital signs through context histories. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03126-8
5. Bantilan, N., Malgaroli, M., Ray, B., et al.: Just in time crisis response: suicide alert system for telemedicine psychotherapy settings. Psychother. Res. 31(3), 289–299 (2021). https://doi.org/10.1080/10503307.2020.1781952
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1. Artificial Intelligence-based Suicide Prevention and Prediction: A Systematic Review (2019-2023);Information Fusion;2024-09
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