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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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When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
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Discovering associations among diagnosis groups using topic modeling.

Ding Cheng Li1, Terry Thermeau1, Christopher Chute1

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Summary
This summary is machine-generated.

Machine learning models like Latent Dirichlet Allocation (LDA) can automatically cluster patient diagnostic groups from electronic medical records (EMR). This unsupervised approach shows promise for epidemiology and biomedical research.

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Area of Science:

  • Biomedical Informatics
  • Epidemiology
  • Machine Learning

Background:

  • Electronic medical records (EMR) are growing rapidly.
  • There is a need to extract patterns from EMR data using data mining and machine learning.
  • Clustering patient diagnostic groups is essential for epidemiological studies.

Purpose of the Study:

  • To apply unsupervised statistical models to cluster patient diagnostic groups from EMR data.
  • To evaluate the potential of Latent Dirichlet Allocation (LDA) for epidemiological research.

Main Methods:

  • Utilized Latent Dirichlet Allocation (LDA), an unsupervised statistical model.
  • Applied LDA to cluster patient diagnostic groups from the Rochester Epidemiology Projects (REP) dataset.

Main Results:

  • Initial results demonstrate LDA's capability to cluster patient diagnostic groups effectively.
  • The model identified distinct patient clusters based on diagnostic data.

Conclusions:

  • Latent Dirichlet Allocation (LDA) shows significant potential for application in epidemiology.
  • The unsupervised nature and interpretive power of LDA make it valuable for biomedical studies.
  • LDA can facilitate pattern discovery in large-scale EMR datasets.