使用可解释的人工智能在COVID-19患者死亡率中交叉验证社会经济差异
Li Shi1, Redoan Rahman1, Esther Melamed2
1School of Information, University of Texas at Austin, Austin, Texas, USA.
概括
可解释的人工智能 (XAI) 方法揭示了COVID-19患者死亡率的社会经济差异. 医疗保险,老年和性别显著影响死亡率预测,突出XAI.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 随着COVID-19的流行,显著的健康差异得到了凸显.
- 了解影响患者死亡率的社会经济因素对于有针对性的干预至关重要.
- 现有的模型往往缺乏透明度,无法识别关键的死亡率预测因素.
研究的目的:
- 应用可解释的人工智能 (XAI) 方法来调查COVID-19患者死亡率中的社会经济差异.
- 使用SHAP和LIME对特征重要性的全球和本地解释进行比较.
- 为了证明XAI在识别和验证用于死亡率预测的特征归属方面的优势.
主要方法:
- 开发了一个使用非识别医院数据集的极端梯度提升 (XGBoost) 预测模型.
- 应用沙普利添加式解释 (SHAP) 和局部可解释模型不可知论解释 (LIME) 对于XAI.
- 对个体患者预测的SHAP和LIME之间的交叉验证特征重要性和解释.
主要成果:
- XAI模型将医疗保险的财务阶级,年龄较大和性别确定为COVID-19死亡率预测的高影响特征.
- 无论是SHAP还是LIME,都提供了一致的全球和本地特征重要性解释.
- 证明了XAI在揭示特征重要性和预测能力方面的能力.
结论:
- XAI方法是揭示健康结果的社会经济差异的有价值的工具.
- 医疗保险状态,年龄和性别是影响COVID-19患者死亡率的关键因素.
- SHAP和LIME之间的一致性支持它们在临床预测模型中的交叉验证特征归属中的实用性.
更多相关视频
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
1.3K
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.1K
相关概念视频
01:21Cancer Survival Analysis
402
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
402
01:21Causality in Epidemiology
500
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
500
01:20Comparing the Survival Analysis of Two or More Groups
228
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
228
01:27Confounding in Epidemiological Studies
196
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
196
01:29Bias in Epidemiological Studies
375
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
375
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K