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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Factors Affecting the Risk of Infection01:26

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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
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Cognitive Dissonance01:38

Cognitive Dissonance

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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GIS Software, Hardware, and Sources of GIS Data01:23

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
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Key Attributes include the following:
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Factors Influencing Drug Absorption: Disease States and Pharmacology01:25

Factors Influencing Drug Absorption: Disease States and Pharmacology

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Multiple disease states can significantly influence the oral drug absorption process by affecting blood flow and the functionality of the gastrointestinal (GI) system. Various GI diseases, including conditions that alter GI motility, such as diarrhea, decreased acid secretions (achlorhydria), and infections, have been associated with reduced drug absorption.
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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Brankica Bratić1, Vladimir Kurbalija2, Mirjana Ivanović2

  • 1Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, Novi Sad, Serbia. brankica.bratic@dmi.uns.ac.rs.

Journal of Medical Systems
|October 29, 2018
PubMed
Summary

Machine learning aids in diagnosing cognitive diseases like Alzheimer's and Parkinson's by revealing hidden symptom-illness links. Further research is needed for early detection and integrating these tools into clinical practice.

Keywords:
Alzheimer’s diseaseCognitive diseasesData miningMachine learningParkinson’s disease

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Area of Science:

  • Life Sciences
  • Medical Informatics
  • Computational Biology

Background:

  • Machine learning and data mining have been applied in life sciences for two decades.
  • Medicine is a prime domain for these techniques, aiding in diagnostic modeling.
  • These methods uncover hidden dependencies between symptoms and illnesses.

Purpose of the Study:

  • To provide a comprehensive overview of machine learning research for cognitive disease prediction.
  • To focus on Alzheimer's disease, mild cognitive impairment, and Parkinson's disease.
  • To analyze state-of-the-art methodologies, data sources, and public datasets.

Main Methods:

  • Survey of recent machine learning research and applications.
  • Comparative analysis of different methodological approaches.
  • Review of data sources and publicly available datasets.

Main Results:

  • Machine learning effectively models diagnostic information using causal and statistical data.
  • Identified various state-of-the-art machine learning techniques applied to cognitive diseases.
  • Comparative analysis of existing approaches and data resources.

Conclusions:

  • Early detection of cognitive diseases remains a significant challenge.
  • Integration of machine learning tools into diagnostic practice and therapy planning is crucial.
  • Future work should focus on advancing early detection and clinical application of ML tools.