Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care.
Journal
Npj mental health research
ISSN: 2731-4251
Titre abrégé: Npj Ment Health Res
Pays: England
ID NLM: 9918592488906676
Informations de publication
Date de publication:
07 Jun 2024
07 Jun 2024
Historique:
received:
24
10
2023
accepted:
25
04
2024
medline:
8
6
2024
pubmed:
8
6
2024
entrez:
7
6
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38849429
doi: 10.1038/s44184-024-00071-0
pii: 10.1038/s44184-024-00071-0
doi:
Types de publication
Journal Article
Langues
eng
Pagination
26Subventions
Organisme : Medical Research Future Fund
ID : MRFAI000097
Organisme : National Health and Medical Research Council
ID : 511921
Organisme : National Health and Medical Research Council
ID : GNT2018157
Informations de copyright
© 2024. The Author(s).
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