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Related Concept Videos

Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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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.
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Methods of Documentation II: POMR01:26

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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Information Processing Approach01:30

Information Processing Approach

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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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The Scientific Method02:40

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Research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
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Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

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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:
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Scientific document processing: challenges for modern learning methods.

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Neural networks struggle with scientific papers due to their structure and multimodal nature. This survey explores deep learning methods addressing these challenges for scholarly document processing (SDP).

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Neural networks excel at web document tasks but face challenges with scientific publications.
  • Scientific documents have unique characteristics: complex discourse structure, interconnectedness, and multimodality.
  • Existing methods often fail to adequately address these specific challenges in scholarly document processing (SDP).

Purpose of the Study:

  • To survey modern neural network learning methods for scholarly document processing (SDP).
  • To identify approaches that can model discourse structure, interconnectivity, and multimodality in scientific documents.
  • To highlight advancements in datasets and tools for deep learning in SDP.

Main Methods:

  • Review of current neural network architectures and learning techniques.
  • Analysis of methods addressing discourse structure, document interconnections, and multimodal data.
  • Examination of large-scale dataset creation and tool development for deep learning in SDP.

Main Results:

  • Identification of neural network approaches capable of handling scientific document complexities.
  • Demonstration of methods that leverage discourse structure, interconnections, and multimodality.
  • Overview of resources facilitating deep learning deployment for SDP.

Conclusions:

  • Deep learning offers promising avenues for advancing scholarly document processing (SDP).
  • Future research should focus on refining models for discourse, interconnections, and multimodality.
  • Continued development of datasets and tools is crucial for effective neural NLP in SDP.