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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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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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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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Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

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Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Related Experiment Video

Updated: Mar 20, 2026

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

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Conditional Disease Development extracted from Longitudinal Health Care Cohort Data using Layered Network

Venkateshan Kannan1,2, Fredrik Swartz1,2,3, Narsis A Kiani1,2

  • 1Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.

Scientific Reports
|May 24, 2016
PubMed
Summary

This study introduces a new method to analyze complex health data, revealing potential causal links between diseases. The findings highlight new clinical relationships and disease development pathways.

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Last Updated: Mar 20, 2026

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

  • Health Informatics
  • Computational Biology
  • Network Medicine

Background:

  • Health care data is valuable for clinical decision support but challenging to analyze due to data complexity and near-synonymous diagnoses.
  • Extracting non-trivial conclusions beyond simple associations from large-scale disease data is difficult.

Purpose of the Study:

  • To develop a systematic methodology for deriving statistically valid conditional disease development.
  • To uncover potential causal relationships and novel pathophysiological associations from electronic health records.

Main Methods:

  • Utilized a large cohort of 5,512,469 individuals followed over 13 years in inpatient care.
  • Introduced a causal information fraction measure and leveraged the composite structure of ICD codes.
  • Extracted a directed, lower-dimensional network representation (100 nodes, 130 edges) of disease interactions.

Main Results:

  • Successfully derived statistically valid conditional disease development pathways.
  • Identified specific disease sequences, such as behavioral disorders preceding prescription drug poisoning.
  • Revealed associations like leiomyoma in women being followed by endometriosis.

Conclusions:

  • The methodology enables the extraction of putative causal relations from complex health data.
  • The findings suggest novel clinical relationships and pathophysiological associations warranting further investigation.
  • This approach can enhance clinical decision support systems by uncovering hidden disease development patterns.