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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Data Reporting and Recording01:24

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:15

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Data Collection II01:29

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat
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[Big data in imaging].

Philipp Sewerin1, Benedikt Ostendorf2, Axel J Hueber3

  • 1Heinrich-Heine-Universität Düsseldorf (HHU), Poliklinik, Funktionsbereich und Hiller-Forschungszentrum für Rheumatologie, Universitätskliniken Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Deutschland. philipp.sewerin@med.uni-duesseldorf.de.

Zeitschrift Fur Rheumatologie
|February 8, 2018
PubMed
Summary
This summary is machine-generated.

Big data and artificial intelligence offer new avenues for medical research beyond traditional clinical trials. While promising for rheumatology imaging, data challenges remain.

Keywords:
Artificial intelligenceComputed tomographyData analysisDecision makingMagnetic resonance imaging

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Area of Science:

  • Medical research
  • Data science
  • Medical imaging

Background:

  • Traditional hypothesis-driven research is limited by cost and time.
  • Clinical trials can only address a limited number of research questions.
  • Big data approaches offer a new paradigm for comprehensive data analysis.

Purpose of the Study:

  • To explore the potential of big data and artificial intelligence in medical research.
  • To assess the suitability of automated imaging analysis for rheumatology.
  • To identify challenges and opportunities in implementing big data solutions for medical imaging.

Main Methods:

  • Leveraging large-scale, digitized data for research.
  • Utilizing automated analysis algorithms and artificial intelligence (AI).
  • Focusing on medical imaging, particularly in rheumatology.

Main Results:

  • AI and automated analysis show high precision in identifying pathologies.
  • Big data analytics can investigate a multitude of variables simultaneously.
  • Individualized risk stratification in rheumatology is a potential application.

Conclusions:

  • Big data and AI present a promising future for medical advancements.
  • Challenges include data heterogeneity and data protection regulations in Germany.
  • Overcoming these hurdles is crucial for realizing the potential of big data in imaging.