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相关概念视频

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
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...
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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 settings,...
Discharge Summary Forms01:31

Discharge Summary Forms

The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
Here's a detailed look at the key components and guidelines for preparing a discharge summary:

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相关实验视频

Updated: Jun 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

协作粗细疾病学习与出院总结意识为EHR事件预测事件的意识.

Yan Kang, Zhuolun Li, Bin Pu

    IEEE transactions on cybernetics
    |March 3, 2026
    PubMed
    概括

    这项研究引入了一个新的框架,通过分析疾病关系和患者笔记来预测电子健康记录 (EHR) 事件. 该模型通过整合层次的诊断代码和非结构化的临床文本来提高预测准确性.

    相关实验视频

    Last Updated: Jun 9, 2026

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
    07:31

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

    Published on: May 15, 2020

    科学领域:

    • 医疗信息学 医疗信息学
    • 人工智能的人工智能
    • 计算生物学 计算生物学

    背景情况:

    • 深度学习模型用于电子健康记录 (EHR) 事件预测.
    • 现有的模型在建模动态疾病关系,利用诊断代码本体学和结合非结构化的临床笔记方面面临挑战.

    研究的目的:

    • 提出一个粗到细的疾病学习框架,包括患者笔记,以提高EHR事件预测.
    • 为了有效地捕捉动态和静态疾病特征.
    • 为了解决目前用于EHR分析的深度学习模型的局限性.

    主要方法:

    • 通过分析同时发生的分布,构建了一个细粒度的动态疾病图.
    • 通过整合层次化的ICD-9-CM代码信息来完善疾病嵌入.
    • 利用封闭的循环单元,基于位置的注意力和软注意力机制.
    • 集成的非结构化出院摘要和辅助患者笔记,用于协作学习.

    主要成果:

    • 与九种基线方法相比,拟议的模型在EHR事件预测方面表现优异.
    • 实验是在两个现实世界EHR数据集上进行的:MIMIC-III和MIMIC-IV.
    • 该框架有效地捕捉了动态的疾病关系,并整合了多视角的本体信息.

    结论:

    • 大致到细致的疾病学习框架在EHR事件预测方面取得了重大进展.
    • 整合层次的诊断代码和非结构化的临床笔记可以提高模型的准确性.
    • 拟议的方法为分析医疗保健中复杂的患者数据提供了可靠的方法.