ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2023
Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.BMC veterinary research · 2025Article
- Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.Critical care explorations · 2025Article
- Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.JAMIA open · 2024Article
- Article
- Advancing Clinical Information Systems: Harnessing Telemedicine, Data Science, and AI for Enhanced and More Precise Healthcare Delivery.Yearbook of medical informatics · 2024Review
- Impact of a deep learning sepsis prediction model on quality of care and survival.NPJ digital medicine · 2024Article
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.Clinical relevance- This platform is currently hosting a deep learning model for the early prediction of sepsis that is operational in two emergency departments.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.