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Related Concept Videos

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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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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ER Retrieval Pathway01:45

ER Retrieval Pathway

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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
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Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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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...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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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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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Document-level medical relation extraction via edge-oriented graph neural network based on document structure and

Tao Li1, Ying Xiong1, Xiaolong Wang1

  • 1Harbin Institute of Technology, Shenzhen, China.

BMC Medical Informatics and Decision Making
|December 31, 2021
PubMed
Summary
This summary is machine-generated.

We developed SKEoG, a novel graph neural network for document-level medical relation extraction. This method leverages document structure and external knowledge to achieve state-of-the-art results on benchmark datasets.

Keywords:
Document structureExternal knowledgeGraph neural networkMedical relation extraction

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

  • Natural Language Processing
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Document-level relation extraction (RE) is crucial for understanding complex medical information.
  • Existing methods often struggle to effectively integrate document structure and external knowledge.

Purpose of the Study:

  • To propose an effective novel method for document-level medical relation extraction.
  • To enhance the performance of medical RE by utilizing document structure and external knowledge.

Main Methods:

  • A novel edge-oriented graph neural network (EoG) named SKEoG was proposed.
  • SKEoG integrates document structure and external knowledge for medical RE.
  • The model was evaluated on the Chemical-Disease Relation (CDR) and Chemical Reactions (CHR) datasets.

Main Results:

  • SKEoG achieved state-of-the-art performance on both datasets.
  • The highest F1-scores were 70.7 on the CDR dataset and 91.4 on the CHR dataset.
  • Both document structure and external knowledge significantly improved performance.

Conclusions:

  • The SKEoG method represents a significant advancement in document-level medical RE.
  • Document structure and external knowledge are vital components for improving RE performance.
  • The choice of knowledge node representation methods is critical for model efficacy.