住院患者深静脉血栓症风险的预测建模:Q学习增强的特征选择模型
Rizeng Li1, Sunmeng Chen1, Jianfu Xia1
1Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
Computers in biology and medicine
|May 1, 2024
概括
预测深静脉血栓塞 (DVT) 对于预防肺栓塞至关重要. 整合QL-CPSACO和SVM的新AI模型在识别DVT风险因素方面实现了95.90%的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 心血管医学 心血管医学
背景情况:
- 深静脉血栓症 (DVT) 是一种严重的疾病,有可能导致致命的肺栓塞.
- 早期DVT检测和风险分层对于及时干预和预防血栓栓塞事件至关重要.
- 确定住院患者的风险决定因素对于主动管理至关重要.
研究的目的:
- 研究住院患者急性下肢DVT的危险因素.
- 开发和验证用于DVT风险预测的先进机器学习模型.
- 整合一个新的Q学习增强殖民地掠夺搜索殖民地优化器 (QL-CPSACO) 与支持矢量机器 (SVM) 进行特征选择.
主要方法:
- 实施bQL-CPSACO-SVM特征选择模型用于DVT风险评估.
- 使用CEC 2017基准函数进行算法优化验证.
- 将开发的模型应用于DVT数据集,用于预测分析和性能评估.
主要成果:
- 拟议的bQL-CPSACO-SVM模型显示DVT的高预测准确度为95.90%.
- 确定的显著的DVT风险预测因素包括D-二次体,正常的血原血时间,原血百分比活性,年龄,先前的DVT病史,白细胞计数和血栓细胞计数.
- 该模型在优化和预测准确性方面的有效性经过实验验证.
结论:
- 开发的AI模型提供了一个非常准确的工具,用于预测住院患者的DVT风险.
- 确定了关键的临床和实验室参数,可以指导入院后的早期风险评估.
- 这种方法为医生在管理DVT和预防并发症方面提供了宝贵的决策支持.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
10:26A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
Published on: June 2, 2015
17.2K
相关概念视频
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:26Pharmacokinetic Models: Comparison and Selection Criterion
69
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
69
01:07End Point Prediction: Gran Plot
318
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
318
01:13Sensitivity, Specificity, and Predicted Value
298
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...
298
02:07Improving Translational Accuracy
10.2K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.2K