使用医院大数据优化和个性化实验室测试解释与应用程序
Ronan Boutin1, Jakez Rolland2, Marie Codet1
1Bio Logbook, 1 rue Julien Videment, 44200 Nantes, France.
概括
这项研究开发了一种个性化的方法来解释实验室测试结果,为血小板计数创造了一个更窄,更准确的参考间隔. 这种方法增强了患者查和长期健康监测.
科学领域:
- 临床化学 临床化学
- 生物信息学是一种生物信息学.
- 医学大数据分析 医学大数据分析
背景情况:
- 传统的实验室医学依赖于95%的参考间隔,经常忽视测试之间的相关性.
- 医院大数据为改进实验室测试解释提供了宝贵的资源.
研究的目的:
- 使用医院大数据优化和个性化实验室数据解释.
- 专注于改善对血小板计数结果的解释.
主要方法:
- 一个算法阶段程序被用来分析来自医院数据库的实验室测试数据.
- 通过考虑相关测试和个体患者变量 (性别,年龄) 来定义"优化和个性化的参考人群".
- 该方法特别应用于血小板计数分析.
主要成果:
- 分析了来自28,082个人的数据,确定了7个相关的实验室测试.
- 建立了一个新的,个性化的95%血小板计数参考区间为 [152-334 × 10^9/L],比传统值减少了27.2%.
- 导出的参考区间表明了潜在的可转移性,在单独的患者队列上得到了验证.
结论:
- 开发的方法为实验室数据解释提供了新的视角.
- 这种方法对患者查和纵向健康监测有重大影响.
相关概念视频
Issues And Trends In Healthcare Delivery System
5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Sensitivity, Specificity, and Predicted Value
284
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
284
Statistical Software for Data Analysis and Clinical Trials
533
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...
533


