Developing and Visualising a Retrieval-Augmented Deep Learning Model for Population Health Management

开发和可视化用于人口健康管理的检索增强深度学习模型

• 批准号: 2905946
• 金额: --
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2905946
• 关键词: Developing; Visualising; Retrieval; Augmented; Deep

Retrieval-based machine learning (ML) models enable data to be supplemented by relevant information, retrieved from auxiliary databases and "memories". This enables the explainability of decisions made by the model and makes previously acquired knowledge accessible to human decision-makers. In healthcare, routinely collected data typically cannot be entirely relied upon to make predictions for specific adverse events. Furthermore, explainable aspects (such as feature attribution) can be misinformed by the complex and incomplete nature of the data. This project will aim to:i. Develop a ML model that uses retrieval-based query augmentation to build outcome predictions based on a multitude of linked data sources.ii. Develop a visual analytics interface that supports feature engineering and decision provenance.

基于检索的机器学习（ML）模型使数据可以通过相关信息补充，并从辅助数据库和“记忆”中检索。这可以解释该模型做出的决策，并使人类决策者可以获取以前获得的知识。在医疗保健中，通常不能完全依靠经常收集的数据来预测特定的不良事件。此外，数据的复杂性和不完整的性质可能会误导可解释的方面（例如特征归因）。该项目的目的是：i。开发一个使用基于检索的查询增强的ML模型来基于多种链接的数据源来构建结果预测。开发一个视觉分析接口，以支持特征工程和决策出处。