Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care.

Varidel, M; Hickie, IB; Prodan, A; Skinner, A; Marchant, R; Cripps, S; Oliveria, R; Chong, MK; Scott, E; Scott, J; Iorfino, F

Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care.

Varidel, M Hickie, IB Prodan, A Skinner, A Marchant, R Cripps, S

Oliveria, R Chong, MK Scott, E Scott, J Iorfino, F

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Npj Ment Health Res, 2024, 3, (1), pp. 26
Issue Date:: 2024-06-07

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Field	Value	Language
dc.contributor.author	Varidel, M
dc.contributor.author	Hickie, IB
dc.contributor.author	Prodan, A
dc.contributor.author	Skinner, A
dc.contributor.author	Marchant, R
dc.contributor.author	Cripps, S https://orcid.org/0000-0003-3207-172X
dc.contributor.author	Oliveria, R
dc.contributor.author	Chong, MK
dc.contributor.author	Scott, E
dc.contributor.author	Scott, J
dc.contributor.author	Iorfino, F
dc.date.accessioned	2025-04-21T23:49:12Z
dc.date.available	2024-04-25
dc.date.available	2025-04-21T23:49:12Z
dc.date.issued	2024-06-07
dc.identifier.citation	Npj Ment Health Res, 2024, 3, (1), pp. 26
dc.identifier.issn	2731-4251
dc.identifier.issn	2731-4251
dc.identifier.uri	http://hdl.handle.net/10453/186932
dc.description.abstract	There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.
dc.format	Electronic
dc.language	eng
dc.publisher	Springer Nature
dc.relation.ispartof	Npj Ment Health Res
dc.relation.isbasedon	10.1038/s44184-024-00071-0
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care.
dc.type	Journal Article
utslib.citation.volume	3
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-21T23:49:10Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	3
utslib.citation.issue	1

Abstract:

There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/186932