High Predictive Performance Models via Semi-Parametric Survival Regression

通过半参数生存回归的高预测性能模型

• 批准号: DP220102249
• 负责人: A/Prof Serigne Lo
• 金额: $ 29.13万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220102249
• 关键词: Predictive; Performance; Models; via; Semi

This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or time to loan default. Innovative computational methods will be developed for fitting these models. Compared to traditional prediction method, this approach allows greater flexibility while being superior in terms of statistical accuracy and bias. Extensive analyses of healthcare data from diverse fields will be undertaken.

该项目将开发新的统计模型，以实现高预测性能。当应用于帮助医生治疗患者时，这些模型允许用户包括用于预测治疗效果的基因或其他生物标记物。当应用于金融中的风险管理时，这些模型能够包括组织或个人的持续财务状况，以预测例如贷款违约的可能性或时间。将开发创新的计算方法来拟合这些模型。与传统的预测方法相比，该方法具有更大的灵活性，同时在统计精度和偏差方面具有优势。将对来自不同领域的医疗数据进行广泛的分析。