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Related Concept Videos

Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
Nursing Clinical Information System01:27

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
In-situ Hybridization02:31

In-situ Hybridization

In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
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A probe is a complementary strand of DNA or RNA that binds to corresponding nucleotide sequences in a cell. Many...

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Related Experiment Video

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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Published on: September 20, 2018

Hybrid methods for improving information access in clinical documents: concept, assertion, and relation

Anne-Lyse Minard1, Anne-Laure Ligozat, Asma Ben Abacha

  • 1LIMSI-CNRS, Orsay Cedex, France.

Journal of the American Medical Informatics Association : JAMIA
|May 21, 2011
PubMed
Summary

Hybrid approaches combining rule-based and machine-learning methods improved medical concept extraction and assertion/relation annotation. These methods achieved high rankings in the i2b2/VA 2010 challenge.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Medical Informatics

Background:

  • Automated extraction of medical concepts and their assertions/relations is crucial for clinical data analysis.
  • The i2b2/VA 2010 challenge provided a benchmark for evaluating such natural language processing (NLP) systems.

Purpose of the Study:

  • To describe and evaluate NLP approaches for medical concept extraction and assertion/relation annotation.
  • To compare the performance of rule-based, machine-learning, and hybrid methods in this task.

Main Methods:

  • Utilized Natural Language Processing (NLP) for feature extraction from clinical texts.
  • Employed Conditional Random Fields (CRFs) for concept extraction.
  • Applied Support Vector Machines (SVMs) for assertion and relation annotation.
  • Tested various combinations of rule-based and machine-learning techniques.

Main Results:

  • Achieved an F-measure of 0.931 for assertion annotation (ranked 5th/21).
  • Obtained an F-measure of 0.709 for relation annotation (ranked 3rd/16).
  • Concept extraction yielded an F-measure of 0.773, not placing in the top 10.

Conclusions:

  • Purely machine-learning methods are sensitive to the quantity and quality of annotated training data.
  • Rule-based methods alone are insufficient for handling novel data types.
  • Hybrid approaches integrating rule-based and machine-learning techniques demonstrate superior performance for medical NLP tasks.