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相关概念视频

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.2K
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...
1.2K
Nursing Diagnosis01:22

Nursing Diagnosis

2.5K
Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
The nursing diagnosis focuses on evidence-based...
2.5K
Nursing Evaluation01:15

Nursing Evaluation

3.2K
The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
3.2K
Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

1.5K
The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data...
1.5K
Analgesia and Pain Management01:25

Analgesia and Pain Management

415
Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
415

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相关实验视频

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Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
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通过机器学习改善慢性疼痛护理诊断:一项绩效评估

Davide Macrì1, Nicola Ramacciati, Carmela Comito

  • 1Author Affiliations: Istituto di Calcolo e Reti ad Alte Prestazioni (Institute for High-Performance Computing and Networking) (Drs Macrì, Comito, and Forestiero); and Department of Pharmacy, Health and Nutritional Sciences, Università della Calabria (Dr Ramacciati), Rende, Cosenza; Residenze Protette Cerreto d'Esi (Residential Care Facility), Kursana lunga vita Coop. Soc. ONLUS, Cerreto d'Esi, Ancona (Dr Metlichin); and Nursing School, University of Perugia (Dr Giusti); and Servizio Formazione e Qualità, Azienda Ospedaliera di Perugia (Dr Giusti), Perugia, Italy.

Computers, informatics, nursing : CIN
|March 20, 2025
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概括

机器学习有效地分类慢性疼痛在意大利护理笔记. XGBoost的表现优于其他算法,显示了AI.

关键词:
深度学习是一种深度学习.梯度增强可以提高梯度.机器学习 机器学习护理诊断是一个护理诊断.护理笔记 护理笔记预测模型的预测模型.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 慢性疼痛的分类对于有效的患者护理至关重要.
  • 将人工智能 (AI) 整合到医疗保健中可以增强临床决策.
  • 处理意大利医学语言对人工智能模型提出了独特的挑战.

研究的目的:

  • 用意大利护理笔记来评估慢性疼痛分类的机器学习算法.
  • 为了验证慢性疼痛的护理诊断.
  • 探索AI在意大利医疗保健环境中的潜力.

主要方法:

  • 使用网格搜索优化了三个机器学习算法 (XGBoost,梯度提升,BERT).
  • 对每个模型进行了超参数调整.
  • 使用科恩的 κ 系数来比较算法性能.

主要成果:

  • 在分类慢性疼痛方面,XGBoost表现出卓越的性能.
  • 伯特显示出处理复杂的意大利语言结构的潜力.
  • 限制包括BERT的数据量和域特异性.

结论:

  • 对成功的临床AI应用程序而言,算法选择至关重要.
  • 机器学习在改善意大利医疗保健方面具有重大潜力.
  • 未来的工作应该考虑用于增强慢性疼痛分类的多式联络数据.