KGDAL Article Swipe
Lucas J. Liu
,
Victor Ortiz-Soriano
,
Javier A. Neyra
,
Jin Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3459930.3469513
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3459930.3469513
With the rapid accumulation of electronic health record (EHR) data, deep learning (DL) models have exhibited promising performance on patient risk prediction. Recent advances have also demonstrated the effectiveness of knowledge graphs (KG) in providing valuable prior knowledge for further improving DL model performance. However, it is still unclear how KG can be utilized to encode high-order relations among clinical concepts and how DL models can make full use of the encoded concept relations to solve real-world healthcare problems and to interpret the outcomes. We propose a novel knowledge graph guided double attention LSTM model named KGDAL for rolling mortality prediction for critically ill patients with acute kidney injury requiring dialysis (AKI-D). KGDAL constructs a KG-based two-dimension attention in both time and feature spaces. In the experiment with two large healthcare datasets, we compared KGDAL with a variety of rolling mortality prediction models and conducted an ablation study to test the effectiveness, efficacy, and contribution of different attention mechanisms. The results showed that KGDAL clearly outperformed all the compared models. Also, KGDAL-derived patient risk trajectories may assist healthcare providers to make timely decisions and actions. The source code, sample data, and manual of KGDAL are available at https://github.com/lucasliu0928/KGDAL.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3459930.3469513
- OA Status
- green
- Cited By
- 8
- References
- 39
- Related Works
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- OpenAlex ID
- https://openalex.org/W3191090955
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3191090955Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3459930.3469513Digital Object Identifier
- Title
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KGDALWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-07-30Full publication date if available
- Authors
-
Lucas J. Liu, Victor Ortiz-Soriano, Javier A. Neyra, Jin ChenList of authors in order
- Landing page
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https://doi.org/10.1145/3459930.3469513Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/8445228Direct OA link when available
- Concepts
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Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 2, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 11 |
| abstract_inverted_index.patients | 104 |
| abstract_inverted_index.problems | 78 |
| abstract_inverted_index.utilized | 53 |
| abstract_inverted_index.valuable | 35 |
| abstract_inverted_index.attention | 92, 117, 157 |
| abstract_inverted_index.available | 195 |
| abstract_inverted_index.conducted | 144 |
| abstract_inverted_index.datasets, | 131 |
| abstract_inverted_index.decisions | 182 |
| abstract_inverted_index.different | 156 |
| abstract_inverted_index.efficacy, | 152 |
| abstract_inverted_index.exhibited | 15 |
| abstract_inverted_index.improving | 40 |
| abstract_inverted_index.interpret | 81 |
| abstract_inverted_index.knowledge | 30, 37, 88 |
| abstract_inverted_index.mortality | 99, 140 |
| abstract_inverted_index.outcomes. | 83 |
| abstract_inverted_index.promising | 16 |
| abstract_inverted_index.providers | 178 |
| abstract_inverted_index.providing | 34 |
| abstract_inverted_index.relations | 57, 73 |
| abstract_inverted_index.requiring | 109 |
| abstract_inverted_index.constructs | 113 |
| abstract_inverted_index.critically | 102 |
| abstract_inverted_index.electronic | 5 |
| abstract_inverted_index.experiment | 126 |
| abstract_inverted_index.healthcare | 77, 130, 177 |
| abstract_inverted_index.high-order | 56 |
| abstract_inverted_index.prediction | 100, 141 |
| abstract_inverted_index.real-world | 76 |
| abstract_inverted_index.mechanisms. | 158 |
| abstract_inverted_index.performance | 17 |
| abstract_inverted_index.prediction. | 21 |
| abstract_inverted_index.accumulation | 3 |
| abstract_inverted_index.contribution | 154 |
| abstract_inverted_index.demonstrated | 26 |
| abstract_inverted_index.outperformed | 165 |
| abstract_inverted_index.performance. | 43 |
| abstract_inverted_index.trajectories | 174 |
| abstract_inverted_index.KGDAL-derived | 171 |
| abstract_inverted_index.effectiveness | 28 |
| abstract_inverted_index.two-dimension | 116 |
| abstract_inverted_index.effectiveness, | 151 |
| abstract_inverted_index.https://github.com/lucasliu0928/KGDAL. | 197 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.82527942 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |