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相关概念视频

Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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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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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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相关实验视频

Updated: May 15, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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使用cSPADE算法在电子医疗记录中发现多种疾病之间的顺序模式和相互关系.

He Ma1,2, Qianxin Huang3, Hong Zhang4

  • 1School of Information and Control Engineering, China University of Mining and Technology, No.1 Daxue Road, Xuzhou, 221000, Jiangsu, P.R. China.

Archives of public health = Archives belges de sante publique
|April 11, 2025
PubMed
概括

这项研究揭示了显著的连续性疾病模式和诊断之间的时间间隔,为共患病提供了洞察力. 结果突出了性别特异性疾病进展,有助于临床决策支持.

关键词:
伴随性疾病 伴随性疾病连续的模式采矿采矿.cSPADE 是一个SPADE.

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科学领域:

  • 计算流行病学计算流行病学
  • 医疗信息学 医疗信息学
  • 生物统计学 生物统计学

背景情况:

  • 了解疾病发病序列对于伴随性疾病研究和预测患者结果至关重要.
  • 时间性疾病关系为疾病进展和干预策略提供了信息.

研究的目的:

  • 通过使用顺序模式挖矿来调查相互依赖性和慢性疾病顺序.
  • 分析不同疾病发作之间的时间间隔.
  • 检查疾病序列模式的基于性别的差异.

主要方法:

  • 利用了269973名患者 (2012-2022) 的电子病历数据.
  • 采用了使用等效类 (SPADE) 算法的顺序模式发现.
  • 分析了1,060,344个诊断条目与国际疾病分类第十次修订 (ICD-10) 代码.

主要成果:

  • 确定了212个重要的顺序性并发症模式,主要涉及内分泌和循环系统.
  • 疾病发病间隔从2个月以下到5-10年不等,其中许多在1-2年之间.
  • 176个模式在男性中表现出更强的支持;心血管/肝脏疾病在男性中更常见,骨科/内分泌在女性中更常见.

结论:

  • 受约束的SPADE (cSPADE) 算法有效地揭示了临床相关的顺序性并发症模式.
  • 识别的模式可以促进疾病预防,病因学研究和临床决策支持系统.