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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Human Genetics01:28

Human Genetics

Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Related Experiment Video

Updated: Jun 16, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Predicting psychopathology symptom trajectories using machine learning: a 33-year prospective study.

Seda Sacu1,2, Fabian Streit2,3,4, Stephanie H Witt2,5

  • 1Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.

Journal of Child Psychology and Psychiatry, and Allied Disciplines
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study identified distinct psychopathology symptom trajectories from childhood to adulthood, predicting them using genetic and environmental risk factors. Understanding these patterns aids early identification and targeted interventions for mental health.

Keywords:
Symptom trajectoriesdevelopmental psychopathologyexternalizing symptomsgrowth mixture modelsinternalizing symptomsmachine learning

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Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

Area of Science:

  • Developmental Psychology
  • Psychopathology Research
  • Machine Learning in Health

Background:

  • Previous research links psychopathology symptoms to psychosocial risk but often lacks adult data or comprehensive measures.
  • This study addresses the gap by examining long-term symptom trajectories and predictive factors into adulthood.

Purpose of the Study:

  • To identify distinct developmental trajectories of externalizing and internalizing psychopathology symptoms over 25 years.
  • To predict these trajectories using a comprehensive set of genetic and environmental risk factors via machine learning.

Main Methods:

  • Longitudinal birth cohort data (N=317) with symptom assessments from age 8 to 33.
  • Growth mixture models to identify symptom trajectories.
  • Machine learning models trained on polygenic scores, psychosocial factors, and temperament.

Main Results:

  • Three trajectories identified for both externalizing and internalizing symptoms: low, increasing, and decreasing.
  • Genetic (polygenic scores) and environmental (family, social factors) predictors were significant.
  • Machine learning models showed fair to modest discriminatory power (AUCs 0.75-0.88).

Conclusions:

  • Psychopathology symptom trajectories are diverse and evolve across development.
  • A combination of genetic and environmental factors influences these trajectories.
  • Identifying these risk factors is crucial for early detection and tailored interventions.