概括
与传统因素相比,电子健康记录 (EHR) 模型显著改善了癌症风险预测. 这些先进的模型通过识别更多的高风险个体进行查来增强早期检测.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 目前的癌症查指南使用年龄或吸烟史等有限的标准.
- 电子健康记录 (EHR) 提供了广泛的纵向患者数据,以改善风险预测.
- 基于电子健康记录的模型与癌症查的传统风险因素进行比较的证据有限.
研究的目的:
- 评估基于EHR的预测模型与传统风险因素的临床实用性,以识别患癌症高风险的个体.
- 为了比较基线EHR模型和先进EHR基础模型在癌症风险分层中的有效性.
主要方法:
- 基于EHR的预测模型与传统风险因素 (基因突变,家族病史) 的系统评估.
- 利用了来自我们所有人研究计划 (>865,000名参与者) 的数据,整合了EHR,基因组和调查数据.
- 通过使用EHR基础模型,评估了八种主要癌症类型和26种癌症类型的扩展组的预测性能.
主要成果:
- 基线基于EHR的模型显示,与传统风险因素相比,真正的癌症病例的丰富度是传统风险因素的3至6倍.
- 无论是独立使用还是与传统因素相辅相成,EHR模型都表现出卓越的性能.
- 先进的EHR基础模型进一步提高了26种癌症类型的预测准确性.
结论:
- 基于EHR的预测建模为识别癌症查高风险个体提供了更有效的策略.
- 在全面的患者数据上训练的基础模型显示了精确和可扩展的早期癌症检测的巨大潜力.
- 将EHR数据集成到预测模型中可以克服当前查指南的局限性.
相关概念视频
Criteria for Causality: Bradford Hill Criteria - II
1.3K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.3K
Criteria for Causality: Bradford Hill Criteria - I
1.1K
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
1.1K
Predicting Molecular Geometry
46.0K
VSEPR Theory for Determination of Electron Pair Geometries
46.0K
Self-Evaluation Maintenance Model
322
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
322
Prediction Intervals
3.4K
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.
3.4K
Decision Making: Traditional Method
5.4K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.4K


