Bettencourt-Silva Joao, Mulligan Natasha, Cullen Conor, Kotoulas Spyros
IBM Research Ireland.
IBM Watson Health.
Stud Health Technol Inform. 2018;255:70-74.
There is a growing interest in identifying, weighing and accounting for the impact of health determinants that lie outside of the traditional healthcare system, yet there is a remarkable paucity of data and sources to sustain these efforts. Decision support systems would greatly benefit from leveraging models which are able to extend and use such cross-domain knowledge. This paper describes an approach to identify and explore related social and clinical terms based on large corpora of unstructured data. Using word embedding techniques on relevant sources of knowledge, we have identified terms that appear close together in the high-dimensional space. In particular, having created a model with cross-domain knowledge on the social determinants of health, we have been able to demonstrate that it is possible to surface terms in this domain when querying for related clinical terms, thereby creating a bridge between the social and clinical determinants of health. This is a promising approach with significant applicability in decision support efforts in healthcare.
人们越来越关注识别、权衡和考量传统医疗系统之外的健康决定因素的影响，但用于支持这些工作的数据和资源却极为匮乏。决策支持系统将极大受益于利用能够扩展和运用此类跨领域知识的模型。本文描述了一种基于非结构化数据的大型语料库来识别和探索相关社会与临床术语的方法。通过对相关知识源运用词嵌入技术，我们识别出在高维空间中紧密相邻的术语。特别是，在创建了一个关于健康社会决定因素的跨领域知识模型后，我们已经能够证明，在查询相关临床术语时，可以在这个领域中呈现出相关术语，从而在健康的社会决定因素和临床决定因素之间架起一座桥梁。这是一种很有前景的方法，在医疗保健决策支持工作中具有显著的适用性。
