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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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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 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.
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Machine learning to parse breast pathology reports in Chinese.

Rong Tang1, Lizhi Ouyang2, Clara Li3

  • 1Division of Surgical Oncology, MGH, Boston, USA.

Breast Cancer Research and Treatment
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PubMed
Summary

This study developed a machine learning model to automatically extract structured data from Chinese breast pathology reports. The model achieved high accuracy, demonstrating scalability for parsing pathology reports in multiple languages.

Keywords:
ChineseElectronic health record (EHR)Machine learningNatural language processing (NLP)Pathology reports

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Area of Science:

  • Bioinformatics
  • Computational Pathology
  • Natural Language Processing

Background:

  • Structured pathology databases are crucial for clinical insights but are labor-intensive to create.
  • Automating the extraction of pathology findings from reports is essential for efficient data utilization.
  • Existing machine learning efforts primarily focus on English pathology reports.

Purpose of the Study:

  • To develop an automated approach for constructing structured databases from Chinese breast pathology reports.
  • To adapt and evaluate machine learning techniques for natural language processing in the Chinese language.
  • To establish a foundation for extending automated extraction to other languages.

Main Methods:

  • Collected 2104 Chinese breast pathology reports.
  • Annotated binary and numerical pathologic entities by native Chinese-speaking physicians.
  • Utilized a natural language processing algorithm trained on 1216 cases and refined on 405 cases, with testing on 405 cases.
  • Extracted 13 binary and 8 numerical entities.

Main Results:

  • The model achieved high per-entity accuracy (91-100%) compared to physicians.
  • Overall accuracy for binary entities was 98% and for numerical entities was 95%.
  • On a per-report basis, 85% of test reports were completely accurate for binary entities with sufficient training data.

Conclusions:

  • Chinese breast pathology reports can be automatically parsed into structured data using machine learning.
  • The study validates the scalability of techniques effective in English to other languages.
  • Automated parsing facilitates the creation of large-scale pathology databases in Chinese.