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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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Nursing Interventions I: Taxonomy of Nursing Interventions01:03

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Nursing interventions are chosen as part of the planning process to achieve patient outcomes. Once nursing diagnoses are determined, the goals and outcomes are specified, then the nursing interventions are selected and individualized according to the patient's situation.
A nursing intervention is a treatment or action based on scientific concepts and knowledge from the nursing, behavioral, and physical sciences. Identifying and prioritizing nursing interventions based on the desired outcome...
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Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

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Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
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Longitudinal Studies01:26

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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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Trial and Error and Algorithm01:12

Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Community Based Intervention01:30

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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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Long-Term Culture of Individual Caenorhabditis elegans on Solid Media for Longitudinal Fluorescence Monitoring and Aversive Interventions
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eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data.

Venkata Sukumar Gurugubelli1,2, Zhouzhou Li3,2, Honggang Wang3

  • 1Department of Computer and Information Science, University of Massachusetts - Dartmouth, Dartmouth, MA, 02747.

International Conference on Computing, Networking, and Communications : [Proceedings]. International Conference on Computing, Networking and Communications
|March 26, 2019
PubMed
Summary

This study introduces an enhanced Fuzzy C-means (eFCM) clustering method to improve analysis of complex longitudinal intervention data. The new method offers better computational efficiency and avoids local optimization issues in clustering.

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Area of Science:

  • Data Science
  • Machine Learning
  • Biostatistics

Background:

  • Clustering methods are crucial for analyzing heterogeneity in treatment effects, particularly in longitudinal behavioral intervention studies.
  • Existing methods like K-means and Fuzzy C-means (FCM) are widely used but face challenges with high-dimensional data, missing values, and initialization issues.
  • The MIFuzzy framework aims to address these methodological challenges in analyzing longitudinal intervention data.

Purpose of the Study:

  • To propose a novel initialization method for Fuzzy C-means (FCM) to overcome local optima and reduce convergence time.
  • To enhance the MIFuzzy framework by incorporating an improved FCM algorithm (eFCM) for high-dimensional longitudinal intervention data with missing values.
  • To address overlapping clusters and improve the analysis of complex datasets.

Main Methods:

  • Developed an enhanced Fuzzy C-means clustering (eFCM) algorithm inspired by K-means++ initialization.
  • Integrated eFCM into the existing MIFuzzy framework to handle high-dimensional longitudinal data with missing values.
  • Evaluated the proposed method on real-world longitudinal intervention data and standard benchmark datasets.

Main Results:

  • The enhanced Fuzzy C-means (eFCM) method demonstrates improved computational efficiency compared to conventional FCM.
  • eFCM effectively avoids the problem of local optimization, leading to more robust clustering results.
  • The method successfully handles high-dimensional longitudinal data with missing values and overlapping clusters.

Conclusions:

  • The proposed eFCM integrated into MIFuzzy offers a significant advancement for analyzing complex longitudinal intervention data.
  • This approach enhances the reliability and efficiency of clustering in behavioral intervention studies with missing data.
  • The findings suggest eFCM is a valuable tool for identifying distinct patient groups and understanding treatment effect heterogeneity.