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Related Concept Videos

Machines01:19

Machines

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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.
A free-body diagram of the...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Predator-Prey Interactions02:39

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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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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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Related Experiment Video

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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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Interactive Machine Learning for Laboratory Data Integration.

Nathanael Fillmore1, Nhan Do1,2, Mary Brophy1,2

  • 1Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary

We developed an interactive machine learning tool to improve the accuracy and efficiency of clinical concept adjudication for laboratory test results. This tool enhances data reuse in secondary research by streamlining the process for expert adjudicators.

Keywords:
Clinical Laboratory Information SystemsSupervised Machine LearningSystems Integration

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Clinical Data Management

Background:

  • Electronic health records (EHRs) contain valuable laboratory data for secondary research.
  • Large-scale data warehouses, like the US Department of Veterans Affairs', house billions of lab tests.
  • Aggregating and precisely retrieving specific lab results from these warehouses is challenging.

Purpose of the Study:

  • To develop an interactive machine learning tool to assist expert adjudicators in identifying relevant laboratory test records.
  • To enhance the efficiency and accuracy of clinical concept adjudication.
  • To facilitate greater data reuse in secondary research by improving data accessibility and quality.

Main Methods:

  • Development of an interactive, user-facing machine learning tool for laboratory test classification.
  • Integration of automatic classification within a comprehensive adjudication workflow.
  • Focus on expert laboratory test adjudicators to "extend their reach".

Main Results:

  • The tool provides automated laboratory classification to support expert adjudication.
  • It aims to lower collaboration barriers and increase transparency in the adjudication process.
  • The system is designed to promote efficiencies and enable broader data reuse.

Conclusions:

  • Interactive machine learning tools can significantly improve clinical concept adjudication for laboratory data.
  • This approach enhances the utility of EHR data for secondary research.
  • The developed tool offers a scalable solution for managing and utilizing large laboratory datasets.