- Tracking hemodynamic responses to treatment and stimuli over long periods remains a grand challenge. Moving from established single-heartbeat technology to longitudinal profiles would require continuous data describing how the patient's state evolves, new methods to extend the temporal domain over which flow is sampled, and high-throughput computing resources. While personalized digital twins can accurately measure 3D hemodynamics over several heartbeats, state-of-the-art methods would require 250 years of wallclock time on leadership scale systems to simulate one month of activity. To address these challenges, we propose a cloud-based, parallel-in-time framework leveraging continuous data from wearable devices to capture the first 3D patient-specific, longitudinal hemodynamic maps. We demonstrate ... [Read More]
- Total Size
- 9 files (17.9 GB)
- Data Citation
- Tanade, C., Rakestraw, E., Ladd, W., Draeger, E., Randles, A. (2023). Data from: Cloud computing to enable wearable-driven longitudinal hemodynamic maps. Duke Research Data Repository. https://doi.org/10.7924/r4f76jd8n
- DOI
- 10.7924/r4f76jd8n
- Subject
- Publication Date
- April 19, 2023
- ARK
- ark:/87924/r4f76jd8n
- Publisher
- Type
- Related Materials
- Contact
- Amanda Randles, amanda.randles@duke.edu
- Title
- Data from: Cloud computing to enable wearable-driven longitudinal hemodynamic maps
- Repository
| Thumbnail | Title | Date Uploaded | Visibility | Actions |
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clustering.zip | 2023-04-19 | Download | |
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lhm.zip | 2023-04-19 | ||
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lhmfc_over_time.zip | 2023-04-19 | Download | |
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lhmf_validation.zip | 2023-04-19 | Download | |
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performance.zip | 2023-04-19 | Download | |
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pre_flow_data.zip | 2023-04-19 | ||
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README.txt | 2023-04-19 | Download | |
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tempconv.zip | 2023-04-19 | Download | |
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testrun.zip | 2023-04-19 | Download |