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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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An integrated, ontology-driven approach to constructing observational databases for research.

William Hsu1, Nestor R Gonzalez2, Aichi Chien1

  • 1Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.

Journal of Biomedical Informatics
|March 31, 2015
PubMed
Summary

An ontology-driven approach improves electronic health record (EHR) data analysis for research. This method enhances data consistency and aids in identifying clinical predictors for conditions like intracranial aneurysms (ICAs).

Keywords:
Biomedical ontologyData extractionDatabaseImage analysisIntracranial aneurysmRetrospective study

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Area of Science:

  • Biomedical Informatics
  • Clinical Data Science

Background:

  • Electronic health records (EHRs) contain valuable clinical data but suffer from incompleteness and inconsistency, hindering secondary use.
  • Challenges in data quality impede retrospective studies and comparative effectiveness research.

Purpose of the Study:

  • To present an ontology-driven approach for longitudinal and continuous extraction and analysis of EHR data.
  • To demonstrate how ontologies can standardize data representation and integrate phenotypes for improved research.

Main Methods:

  • Developed an ontology to guide data extraction and analysis from EHRs.
  • Applied the approach to study factors influencing intracranial aneurysm (ICA) growth and rupture.
  • Captured data for 78 individuals with 120 aneurysms.

Main Results:

  • The ontology facilitated consistent data representation and phenotype integration.
  • Enabled studies assessing relationships between aneurysm characteristics, gene expression, and rupture.
  • Highlighted data quality and workflow challenges in learning health systems.

Conclusions:

  • Ontology-driven methods offer a robust framework for analyzing complex EHR data.
  • Improved data quality and analysis workflows are crucial for advancing a learning health system paradigm.
  • This approach supports identifying clinical predictors for outcomes such as intracranial aneurysm growth and rupture.