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Related Concept Videos

Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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What Are Outliers?01:12

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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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Related Experiment Video

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Long-distance disorder-disorder relation extraction with bootstrapped noisy data.

Yucong Lin1, Yang Li2, Keming Lu3

  • 1Center for Statistical Science, Tsinghua University, Beijing, Beijing, China; Department of Industrial Engineering, Tsinghua University, Beijing, Beijing, China.

Journal of Biomedical Informatics
|August 11, 2020
PubMed
Summary

This study introduces a new long-distance relation extraction algorithm for artificial intelligence in healthcare. The method improves the identification of crucial diagnostic relationships, enhancing medical knowledge graphs.

Keywords:
Article structure embeddingDistant supervisionGraph informationRelation extraction

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Healthcare AI relies on knowledge graphs, but many crucial relations are missing.
  • Existing knowledge graphs have gaps in representing complex medical relationships.

Purpose of the Study:

  • To develop a novel long-distance relation extraction algorithm for healthcare AI.
  • To improve the identification of diagnostic relations like 'may cause' and 'differential diagnosis'.

Main Methods:

  • Utilized bootstrapped noisy data and an extended sentence form for training.
  • Incorporated article section structure and graph information into the model.
  • Applied an attention mechanism to mitigate noise in labeled data.

Main Results:

  • The extended sentence form increased relation and sentence discovery by 1.75x and 2.17x, respectively.
  • The proposed model achieved 9 and 13 percentage points higher accuracy than baseline deep learning and traditional machine learning models, respectively.

Conclusions:

  • The bootstrap data preparation and extended sentence form facilitate large-scale training dataset creation.
  • Section structure embedding and graph information significantly boost prediction accuracy for relation extraction.