Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.
Julian Wenzel, Nils Dreschke, Esther Hanssen, Marlene Rosen, Andrej Ilankovic, Joseph Kambeitz, Anne-Kathrin Fett, Lana Kambeitz-Ilankovic
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引用次数: 0
Abstract
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk toward psychotic illness and identify individuals with need for extended healthcare. Individuals with psychotic disorder (PD, N = 55), healthy individuals (HC, N = 25) and HC with first-degree relatives with psychosis (RE, N = 20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (NPD = 12, NRE = 1, NHC = 2) showed higher mean EMA symptom ratings as compared to cluster 2 (NPD = 43, NRE = 19, NHC = 23) (p < 0.001). Cluster 1 showed a higher burden on negative (p < 0.05) and positive (p < 0.05) psychotic symptoms in cross-sectional PANSS and CAPE ratings than cluster 2. Findings indicate a separation of PD with high symptom burden (cluster 1) from PD with healthy-like rating patterns grouping together with HC and RE (cluster 2). Individuals in cluster 1 might particularly profit from exchange with a clinician underlining the idea of EMA as clinical monitoring tool.
期刊介绍:
The original papers published in the European Archives of Psychiatry and Clinical Neuroscience deal with all aspects of psychiatry and related clinical neuroscience.
Clinical psychiatry, psychopathology, epidemiology as well as brain imaging, neuropathological, neurophysiological, neurochemical and moleculargenetic studies of psychiatric disorders are among the topics covered.
Thus both the clinician and the neuroscientist are provided with a handy source of information on important scientific developments.