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Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
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Schizophrenia, a term introduced by Swiss psychiatrist Eugen Bleuler in 1911, describes a severe psychological disorder marked by profound disruptions in attention, thought processes, language, emotion, and interpersonal relationships. The core feature of schizophrenia is psychosis — a state characterized by a fundamental detachment from reality. This disconnection manifests through distorted logic, impaired perception, and atypical behavior, severely affecting the lives of those...
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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
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Related Experiment Video

Updated: May 29, 2025

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
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Forecasting mental states in schizophrenia using digital phenotyping data.

Thierry Jean1,2, Rose Guay Hottin1, Pierre Orban1,2

  • 1Research Center of the Montreal Mental Health University Institute, Montreal, Canada.

PLOS Digital Health
|February 7, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can forecast mental states using digital phenotyping data. Ordinal regression models, accounting for data imbalance, offer comparable performance to binary classification for richer, interpretable predictions.

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Area of Science:

  • Digital phenotyping
  • Machine learning in mental health
  • Psychiatric data analysis

Background:

  • Machine learning (ML) shows promise for forecasting mental states in psychiatric populations.
  • Previous studies often overlooked the ordinal nature of clinical ratings and data imbalance.

Purpose of the Study:

  • To evaluate ML algorithms for predicting mental states using digital phenotyping data.
  • To compare ordinal regression and binary classification for mental state forecasting.
  • To assess the impact of forecast horizon and algorithm choice (XGBoost vs. LSTM) on predictive performance.

Main Methods:

  • Trained 120 ML models on the CrossCheck dataset (6,364 surveys, 23,551 sensor days) from schizophrenia patients.
  • Utilized ordinal regression and binary classification tasks with XGBoost and LSTM algorithms.
  • Evaluated models across same-day, next-day, and next-week forecast horizons.

Main Results:

  • Most models significantly outperformed baseline, with balanced accuracy between 58%-73%.
  • Metrics ignoring data imbalance overestimated performance.
  • XGBoost models performed comparably to or better than LSTM models.
  • Performance slightly decreased with longer forecast horizons.

Conclusions:

  • Ordinal regression models, using appropriate imbalance-aware metrics, provide clinically valuable and interpretable predictions.
  • These models match binary classification performance without losing information from self-reports.
  • Digital phenotyping combined with appropriate ML techniques can enhance psychiatric clinical practice.