Using deep learning to improve clinical decision support for patient monitoring
Official title: Deep-CDS: Deep Learning Semantic Data Lake for Clinical Decision Support
Funded at Infotech Soft, INC. · NIH-11045748 · Data updated 2026-11-12
This study is testing a new online tool that helps doctors keep a close eye on patients' health and quickly spot any problems, especially for those in intermediate care units, to help prevent serious issues like sepsis and improve patient outcomes.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Infotech Soft, INC. NIH-funded |
| Lab location | 1 site (Miami, United States) |
| Project start | September 10, 2022 |
| Project end | August 31, 2025 |
| Funding year | 2025 |
| Award (this year) | $346,252 |
| NIH project number | 5R44GM143996-04 |
| Project ID | NIH-11045748 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients admitted to hospitals, particularly those in intermediate care units at risk of sepsis or other acute conditions.
✕Less likely to help
Patients who are not admitted to hospitals or those with stable conditions that do not require intensive monitoring may not benefit from this research.
What this research studies
This research focuses on developing a cloud-based deep learning system called Deep-CDS, which aims to enhance clinical decision support by monitoring and predicting patient health deterioration. By utilizing advanced algorithms, the system will analyze patient data in real-time to identify risks and provide alerts for timely interventions. This approach is particularly important for patients in intermediate care units, where monitoring is less intensive than in ICUs. The goal is to reduce mortality rates associated with conditions like sepsis by ensuring that healthcare providers can act quickly based on accurate predictions.
Why it matters
Potential benefit: If successful, this research could significantly reduce mortality rates in hospitals by enabling earlier detection and intervention for deteriorating patients.
How similar studies have gone: Other research has shown success in using AI and machine learning for clinical decision support, indicating that this approach has the potential for meaningful advancements in patient care.
Where this research is happening
-
Infotech Soft, INC.
Miami, United States Active
Researchers
- Principal investigator: Kabuka, Mansur R. — Infotech Soft, INC.
- Study coordinator: Kabuka, Mansur R.
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.