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Clinical Knowledge Graph Embedding Representation Bridging the Gap between Electronic Health Records and Prediction Models

Heng Chung, MW; Liu, J; Tissot, H; (2020) Clinical Knowledge Graph Embedding Representation Bridging the Gap between Electronic Health Records and Prediction Models. In: Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). (pp. pp. 1448-1453). IEEE: Boca Raton, FL, USA. Green open access

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Abstract

Learning knowledge embedding representation is an increasingly important technology. However, the choice of hyperparameters is seldom justified and usually relies on exhaustive search. Understanding the effect of hyperparameter combinations on embedding quality is crucial to avoid the inefficient process and enhance practicality of embedding representation along subsequent machine learning applications. This work focuses on translational embedding models for multi-relational categorized data in the clinical domain. We trained and evaluated models with different combinations of hyperparameters on two clinical datasets. We contrasted the results by comparing metric distributions and fitting a random forest regression model. Classifiers were trained to assess embedding representation quality. Finally, clustering was tested as a validation protocol. We observed consistent patterns of hyperparameter preference and identified those that achieved better results respectively. However, results show different patterns regarding link prediction, which is taken as strong evidence that traditional evaluation protocol used for open-domain data does not necessarily lead to the best embedding representation for categorized data.

Type: Proceedings paper
Title: Clinical Knowledge Graph Embedding Representation Bridging the Gap between Electronic Health Records and Prediction Models
Event: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
ISBN-13: 978-1-7281-4550-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICMLA.2019.00237
Publisher version: https://doi.org/10.1109/ICMLA.2019.00237
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: electronic health records, multi relational data, knowledge graphs, embedding representation, link prediction, clustering, classification
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10094228
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