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Related Concept Videos

What Are Outliers?01:12

What Are Outliers?

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review.

Ghayath Janoudi1,2, Mara Uzun Rada3, Deshayne B Fell2

  • 1Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.

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Augmented intelligence can accelerate clinical discovery by identifying unusual patient cases as outliers. This approach, using outlier analysis, promises to advance medical knowledge and uncover new therapies.

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Area of Science:

  • Medical Informatics
  • Clinical Research Methodology
  • Artificial Intelligence in Medicine

Background:

  • Traditional clinical discovery relies on manual identification of rare patient cases.
  • Existing methods are inefficient given modern health data volumes and computational power.
  • Outlier analysis is a proven technique in fields like finance and manufacturing for identifying unique observations.

Purpose of the Study:

  • To propose an augmented intelligence framework for clinical discovery using outlier analysis.
  • To define clinical discoveries as contextual outliers with novelty-based root causes.
  • To explore the current implementation of outlier analysis in obstetric research.

Main Methods:

  • Developed a five-step augmented intelligence framework: population definition, model building, outlier identification, expert investigation, and hypothesis generation.
  • Defined clinical discoveries as information-based contextual outliers.
  • Conducted a systematic review of outlier analysis in obstetric research.

Main Results:

  • Identified two obstetric studies using aggregate outlier analysis for non-discovery purposes.
  • The systematic review indicated limited application of outlier analysis for clinical discovery in obstetrics.
  • Current use of outlier analysis in clinical research, particularly obstetrics, needs further development.

Conclusions:

  • An augmented intelligence framework offers a novel, efficient approach to clinical discovery.
  • Outlier analysis holds significant potential for accelerating the identification of new medical insights.
  • Further research and development are needed to effectively implement outlier analysis in clinical discovery, especially in under-resourced fields like obstetrics.