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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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Longitudinal Research02:20

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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...
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Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
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Predicting Suicidal Behavior From Longitudinal Electronic Health Records.

Yuval Barak-Corren1, Victor M Castro1, Solomon Javitt1

  • 1From the Predictive Medicine Group, Boston Children's Hospital Informatics Program, Boston; the Technion, Israeli Institute of Technology, Haifa, Israel; the Partners Research Information Systems and Computing, Boston; the Department of Psychiatry, Massachusetts General Hospital, Boston; the Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston; the Department of Psychology, Harvard University, Boston; and Harvard Medical School, Boston.

The American Journal of Psychiatry
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PubMed
Summary
This summary is machine-generated.

Longitudinal electronic health record (EHR) data can predict future suicidal behavior risk. This data-driven approach identifies high-risk patients for early intervention, enhancing clinical screening.

Keywords:
Diagnosis And ClassificationSuicide

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Area of Science:

  • Computational psychiatry
  • Health informatics
  • Epidemiology

Background:

  • Electronic health record (EHR) systems contain extensive longitudinal data.
  • Predicting future suicidal behavior is crucial for timely clinical intervention.

Purpose of the Study:

  • To evaluate the utility of historical EHR data for predicting future suicidal behavior.
  • To develop and validate a predictive model for patient suicide risk.

Main Methods:

  • Bayesian models were developed using a retrospective cohort of over 1.7 million patients.
  • Data spanned 15 years (1998-2012) of inpatient and outpatient visits.
  • Suicidal behavior was defined using ICD-9 codes, expert review of EHR notes, and death certificates.

Main Results:

  • The model achieved 33%-45% sensitivity and 90%-95% specificity in predicting suicidal behavior.
  • Predictions were made 3-4 years in advance on average.
  • Key predictors included substance abuse, psychiatric disorders, and less conventional factors like chronic conditions.

Conclusions:

  • Longitudinal EHR data can effectively predict future suicidal behavior risk.
  • This data-driven approach can serve as an early warning system for clinicians.
  • Computerized screening enhances risk prediction beyond individual clinical assessment.