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Related Concept Videos

Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Critical Values01:31

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A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
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Critical Thinking01:19

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Critical thinking involves reflective and productive thinking and the evaluation of evidence. Critical thinkers seek to understand the deeper meaning of ideas, question assumptions, and make independent decisions about what to believe or do. Scientists, for instance, are often critical thinkers. Critical thinking also requires humility about what we know and don't know and the motivation to look beyond the obvious. It is essential for effective problem-solving.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Applying Machine Learning to Pediatric Critical Care Data.

Jon B Williams1, Debjit Ghosh, Randall C Wetzel

  • 1All authors: Children's Hospital Los Angeles, Los Angeles, CA.

Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
|May 5, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning applied to pediatric critical care data successfully identified medically relevant information, including prognosis and diagnosis. This approach offers potential insights into pediatric critical illness.

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Area of Science:

  • Utilizing machine learning and data science in pediatric critical care.
  • Applying unsupervised machine learning algorithms to electronic health records.

Background:

  • Electronic medical record (EMR) data from 11,384 pediatric intensive care unit (PICU) admissions over 10 years were analyzed.
  • K-means clustering was employed to identify patterns and subgroups within the data.

Discussion:

  • The study explored the potential of machine learning to uncover medically pertinent information from complex pediatric critical care data.
  • The analysis focused on identifying distinct patient clusters based on various clinical features.

Key Insights:

  • The k-means clustering algorithm identified 10 distinct clusters with significant prognostic information (p < 0.0001).
  • Cluster membership effectively predicted mortality (AUC = 0.77) and showed non-random distributions of length of stay, ventilation use, and diagnostic categories (p < 0.0001).

Outlook:

  • Machine learning can extract meaningful prognostic, diagnostic, and therapeutic insights from PICU EMR data in an unsupervised manner.
  • Further development of machine learning applications in critical care may enhance understanding of pediatric critical illness mechanisms.