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Related Concept Videos

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.
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Methods of Documentation III: PIE01:21

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Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
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Methods of Documentation I: Source-Oriented Records01:18

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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 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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Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...
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SegClarity: An Attribution-Based XAI Workflow for Evaluating Historical Document Layout Models.

Iheb Brini1,2, Najoua Rahal2, Maroua Mehri1

  • 1Ecole Nationale d'Ingénieurs de Sousse, Laboratory of Advanced Technology and Intelligent Systems (LATIS), Université de Sousse, Sousse 4054, Tunisia.

Journal of Imaging
|December 24, 2025
PubMed
Summary

This study introduces SegClarity, a new workflow and metric (Attribution Concordance Score) to improve the explainability of deep learning models for historical document segmentation, enhancing trust in AI outputs.

Keywords:
attribution mapsdeep neural networksdocument layout analysisevaluation metricsexplainable artificial intelligencesemantic segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Humanities

Background:

  • Deep learning excels at historical document segmentation but lacks explainability, hindering trust.
  • Explainable AI (XAI) techniques generate heatmaps but lack consensus on evaluation metrics.
  • Existing XAI methods often produce maps similar to segmentation outputs, limiting interpretability.

Purpose of the Study:

  • To present SegClarity, a novel workflow for integrating explainability into historical document semantic segmentation.
  • To introduce the Attribution Concordance Score (ACS) for quantitative evaluation of XAI attribution maps.
  • To enhance the transparency and reliability of deep learning models in historical document analysis.

Main Methods:

  • Developed SegClarity workflow combining visual and quantitative evaluations for segmentation tasks.
  • Introduced the Attribution Concordance Score (ACS) as a novel explainability metric.
  • Conducted extensive experiments on historical document datasets using U-Net models and four XAI methods, including comparisons with RISE and MiSuRe.

Main Results:

  • SegClarity workflow significantly improves the interpretability and reliability of deep learning models for historical document segmentation.
  • Extensive qualitative and quantitative evaluations demonstrated the effectiveness of the proposed approach.
  • The workflow showed generalization capabilities on the Cityscapes dataset for urban scene segmentation.

Conclusions:

  • SegClarity provides a robust framework for evaluating XAI in document image analysis.
  • The ACS metric offers quantitative insights into attribution map reliability.
  • The study enhances trust and reproducibility in AI-driven historical document analysis.