Interactive deep metric learning for healthcare cohort discovery
- Publication Type:
- Conference Proceeding
- Citation:
- Communications in Computer and Information Science, 2019, 1127 CCIS pp. 208 - 221
- Issue Date:
- 2019-01-01
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© Springer Nature Singapore Pte Ltd. 2019. Given the continuous growth of large-scale complex electronic healthcare data, a data-driven healthcare cohort discovery facilitated by machine learning tools with domain expert knowledge is required to gain further insights of the healthcare system. Specifically, clustering plays a crucial role in healthcare cohort discovery, and metric learning is able to incorporate expert feedback to generate more fit-for-purpose clustering outputs. However, most of the existing metric learning methods assume all labelled instances already pre-exists, which is not always true in real-world applications. In addition, big data in healthcare also brings new challenges to metric learning on handling complex structured data. In this paper, we propose a novel systematic method, namely Interactive Deep Metric Learning (IDML), which uses an interactive process to iteratively incorporate feedback from domain experts to identify cohorts that are more relevant to a particular pre-defined purpose. Moreover, the proposed method leverages powerful deep learning-based embedding techniques to incrementally gain effective representations for the complex structures inherit in patient journey data. We experimentally evaluate the effectiveness of the proposed IDML using two public healthcare datasets. The proposed method has also been implemented into an interactive cohort discovery tool for a real-world application in healthcare.
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