This project demonstrates how medical corpus hypothesis generation, a knowledge discovery field of AI, can be used to derive new research angles for landscape and urban planners. The hypothesis generation approach herein consists of a combination of deep learning with topic modeling, a probabilistic approach to natural language analysis that scans aggregated research databases for words that can be grouped together based on their subject matter commonalities; the word groups accordingly form topics that can provide implicit connections between two general research terms. The hypothesis generation system AGATHA was used to identify likely conceptual relationships between emerging infectious diseases (EIDs) and deforestation, with the objective of providing landscape planners guidelines for productive research directions to help them formulate research hypotheses centered on deforestation and EIDs that will contribute to the broader health field that asserts causal roles of landscape-level issues. This research also serves as a partial proof-of-concept for the application of medical database hypothesis generation to medicine-adjacent hypothesis discovery.

翻译：本项目展示了如何将医学语料库假设生成（人工智能的一种知识发现领域）应用于为景观与城市规划师推导新的研究视角。本文采用的假设生成方法结合了深度学习与主题建模——一种概率性自然语言分析方法，可扫描聚合研究数据库，根据主题共性对可归类的词汇进行分组；这些词汇组进而构成主题，能在两个广义研究术语之间提供隐含关联。我们利用假设生成系统AGATHA来识别新兴传染病与森林砍伐之间可能的概念性关系，旨在为景观规划师提供富有成效的研究方向指南，帮助其围绕森林砍伐与新兴传染病形成研究假设，从而为明确主张景观层面问题因果作用的更广泛健康领域做出贡献。本研究同时部分验证了将医学数据库假设生成应用于医学相关领域假设发现的可行性。