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）模型使数据能够通过从辅助数据库和“记忆”中检索的相关信息进行补充。这使得模型做出的决策具有可解释性，并使人类决策者能够访问先前获得的知识。在医疗保健中，常规收集的数据通常不能完全依赖于对特定不良事件的预测。此外，可解释的方面（如特征归属）可能会被数据的复杂性和不完全性所误导。这个项目的目标是：1。开发一个ML模型，该模型使用基于检索的查询增强来基于大量链接数据源构建结果预测。开发支持特征工程和决策来源的可视化分析界面。