The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
Abstract
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to high density of the monitoring data, heterogeneous data types and the requirement for interpretable models.
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In the citing paper
“All of these studies have shown that multimodal models outperform single-modality models in predictive performance.”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [18]
In the citing paper
“Intermediate fusion methods modeled each modality separately over time before integrating learned representations using concatenation [57,58] or attention [18,59], but most relied on retrospective alignment within relatively coarse time windows.”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [18]
In the citing paper
“To improve performance and provide a degree of interpretability to the model, an attention mechanism is adopted in 13 studies [6,14,27,31,43,57,64,72,76–78,80,88].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [14]
In the citing paper
“On the contrary, for those outcomes that happen in hospital, such as inpatient mortality [14,57,61,65], length-of-stay [14,83], and intervention prediction [32,55,65], observation windows at hour level are developed.”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [14]
Citations
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Cited by
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Multitask learning and benchmarking with clinical time series data
Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G., & Galstyan, A. (2019) · Scientific Data · doi:10.1038/s41597-019-0103-9 · Cited by 958
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What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
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Background
Metadata
- Authors
- Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O., & Sun, J.
- Affiliations
- Georgia Institute of Technology (US)
- Emory University (US)
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 2565-2573
- OpenAlex
- W2884555939
- Cited by
- 144
- References
- 43
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 15 · 2023: 11 · 2022: 15 · 2021: 26 · 2020: 31 · 2019: 14 · 2018: 1
- FWCI
- 7.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Institutes of Health (1R01MD011682-01, R56HL138415)
- National Science Foundation (IIS-#1418511, CCF-#1533768)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- clinical decision support, intensive care unit, continuous patient monitoring, electrocardiogram, vital signs, drug information, interpretable models, heterogeneous data types
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