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Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing.

ABSTRACT: Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care.

SUBMITTER: Sandini C

PROVIDER: S-EPMC8476129 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing.

Sandini Corrado C Zöller Daniela D Schneider Maude M Tarun Anjali A Armando Marco M Nelson Barnaby B Amminger Paul G PG Yuen Hok Pan HP Markulev Connie C Schäffer Monica R MR Mossaheb Nilufar N Schlögelhofer Monika M Smesny Stefan S Hickie Ian B IB Berger Gregor Emanuel GE Chen Eric Yh EY de Haan Lieuwe L Nieman Dorien H DH Nordentoft Merete M Riecher-Rössler Anita A Verma Swapna S Thompson Andrew A Yung Alison Ruth AR McGorry Patrick D PD Van De Ville Dimitri D Eliez Stephan S

eLife 20210927

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present ...[more]

PMID: 34569937

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Project description:BackgroundIdentifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.MethodsClinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.ResultsA total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.ConclusionsWe developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.

Dataset Information

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing.

Publications

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing.

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