| Autor: |
Tamara P. Lambert, Michael Chan, Jesus Antonio Sanchez-Perez, Mohammad Nikbakht, David J. Lin, Afra Nawar, Syed Khairul Bashar, Jacob P. Kimball, Jonathan S. Zia, Asim H. Gazi, Gabriela I. Cestero, Daniella Corporan, Muralidhar Padala, Jin-Oh Hahn, Omer T. Inan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Biosensors, Vol 14, Iss 2, p 61 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2079-6374 |
| DOI: |
10.3390/bios14020061 |
| Popis: |
Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10−3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10−3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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