Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge Article Swipe
Prithwish Chakraborty
,
James Codella
,
Piyush Madan
,
Ying Li
,
Hu Huang
,
Yoon-Young Park
,
Chao Yan
,
Ziqi Zhang
,
Cheng Gao
,
Steve Nyemba
,
Min Xu
,
Sanjib Basak
,
Mohamed Ghalwash
,
Zach Shahn
,
Parthasararathy Suryanarayanan
,
Italo Buleje
,
Shannon Harrer
,
Sarah Miller
,
Amol Rajmane
,
Colin G. Walsh
,
Jonathan P. Wanderer
,
Gigi Yuen Reed
,
Kenney Ng
,
Daby Sow
,
Bradley Malin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.04377
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.04377
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.
Related Topics
Concepts
Deep learning
Artificial intelligence
Computer science
Machine learning
Domain knowledge
Domain (mathematical analysis)
Recurrent neural network
Representation (politics)
Artificial neural network
Variety (cybernetics)
Feature learning
Deep neural networks
Architecture
Network architecture
Event (particle physics)
Data science
Quantum mechanics
Political science
Computer security
Art
Mathematics
Law
Visual arts
Mathematical analysis
Physics
Politics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/34457127
- OA Status
- green
- Cited By
- 1
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3154434348
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3154434348Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2104.04377Digital Object Identifier
- Title
-
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital DischargeWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-09Full publication date if available
- Authors
-
Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoon-Young Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Min Xu, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin G. Walsh, Jonathan P. Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley MalinList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/34457127Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2104.04377Direct OA link when available
- Concepts
-
Deep learning, Artificial intelligence, Computer science, Machine learning, Domain knowledge, Domain (mathematical analysis), Recurrent neural network, Representation (politics), Artificial neural network, Variety (cybernetics), Feature learning, Deep neural networks, Architecture, Network architecture, Event (particle physics), Data science, Quantum mechanics, Political science, Computer security, Art, Mathematics, Law, Visual arts, Mathematical analysis, Physics, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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