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

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。这使得模型所做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，通常不能完全依赖常规收集的数据来预测特定的不良事件。此外，可解释的方面（如特征属性）可能会被数据的复杂性和不完整性所误导。该项目的目标是：i.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的数据源构建结果预测。开发支持特征工程和决策出处的可视化分析界面。

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.