Comparative visual analytics for assessing medical records with sequence embedding
Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforwa...
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| Published in | Visual informatics (Online) Vol. 4; no. 2; pp. 72 - 85 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.06.2020
Elsevier |
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| Abstract | Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis. |
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| AbstractList | Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis. |
| Author | Tran, Nam K. Lima, Kelly M. Ma, Kwan-Liu Guo, Rongchen Sen, Soman Fujiwara, Takanori Li, Yiran |
| Author_xml | – sequence: 1 givenname: Rongchen surname: Guo fullname: Guo, Rongchen email: rongchen.guo1020@gmail.com organization: Department of Computer Science, Beihang University, Beijing, China – sequence: 2 givenname: Takanori surname: Fujiwara fullname: Fujiwara, Takanori organization: Department of Computer Science, University of California, Davis, United States – sequence: 3 givenname: Yiran surname: Li fullname: Li, Yiran organization: Department of Computer Science, University of California, Davis, United States – sequence: 4 givenname: Kelly M. surname: Lima fullname: Lima, Kelly M. organization: Department of Pathology and Laboratory Medicine, University of California, Davis, United States – sequence: 5 givenname: Soman surname: Sen fullname: Sen, Soman organization: Department of Surgery, University of California, Davis, United States – sequence: 6 givenname: Nam K. surname: Tran fullname: Tran, Nam K. organization: Department of Pathology and Laboratory Medicine, University of California, Davis, United States – sequence: 7 givenname: Kwan-Liu surname: Ma fullname: Ma, Kwan-Liu organization: Department of Computer Science, University of California, Davis, United States |
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| Keywords | Visual analytics Autoencoder Electronic medical records Sequence similarity Self-attention Event sequence data |
| Language | English |
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