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Nehemiah T Liu

Showing results (11-20 of 25) with videos related to

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Shock (Augusta, Ga.)|April 15, 2014
Utility of vital signs, heart rate variability and complexity, and machine learning for identifying the need for lifesaving interventions in trauma patientsNehemiah T Liu, John B Holcomb, Charles E Wade, et al.
The Journal of Trauma and Acute Care Surgery|September 26, 2015
Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approachesNehemiah T Liu, George C Kramer, Muzna N Khan, et al.
The Journal of Trauma and Acute Care Surgery|April 29, 2014
Evaluation of standard versus nonstandard vital signs monitors in the prehospital and emergency departments: results and lessons learned from a trauma patient care protocolNehemiah T Liu, John B Holcomb, Charles E Wade, et al.
Journal of Critical Care|April 27, 2013
Development and validation of a novel fusion algorithm for continuous, accurate, and automated R-wave detection and calculation of signal-derived metricsNehemiah T Liu, Andriy I Batchinsky, Leopoldo C Cancio, et al.
Journal of Burn Care & Research : Official Publication of the American Burn Association|May 15, 2018
Predicting the Ability of Wounds to Heal Given Any Burn Size and Fluid Volume: An Analytical ApproachNehemiah T Liu, Julie A Rizzo, Beth A Shields, et al.
Burns : Journal of the International Society for Burn Injuries|December 15, 2019
Quantifying the effects of wound healing risk and potential on clinical measurements and outcomes of severely burned patients: A data-driven approachNehemiah T Liu, Sarah K Shingleton, Craig A Fenrich, et al.
The Journal of Trauma and Acute Care Surgery|April 29, 2017
The impact of patient weight on burn resuscitationNehemiah T Liu, Craig A Fenrich, Maria L Serio-Melvin, et al.
Medical & Biological Engineering & Computing|November 23, 2013
Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patientsNehemiah T Liu, John B Holcomb, Charles E Wade, et al.
Journal of Burn Care & Research : Official Publication of the American Burn Association|June 25, 2019
Relationship Between Burn Wound Location and Outcomes in Severely Burned Patients: More Than Meets the SizeNehemiah T Liu, Julie A Rizzo, Sarah K Shingleton, et al.
Journal of Burn Care & Research : Official Publication of the American Burn Association|October 15, 2021
Initial Results of the American Burn Association Observational Multicenter Evaluation on the Effectiveness of the Burn NavigatorJulie A Rizzo, Nehemiah T Liu, Elsa C Coates, et al.
Pageof 3

Showing results (11-20 of 25) with videos related to

Sort By:
Pageof 3
Shock (Augusta, Ga.)|April 15, 2014
Utility of vital signs, heart rate variability and complexity, and machine learning for identifying the need for lifesaving interventions in trauma patientsNehemiah T Liu, John B Holcomb, Charles E Wade, et al.
The Journal of Trauma and Acute Care Surgery|September 26, 2015
Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approachesNehemiah T Liu, George C Kramer, Muzna N Khan, et al.
The Journal of Trauma and Acute Care Surgery|April 29, 2014
Evaluation of standard versus nonstandard vital signs monitors in the prehospital and emergency departments: results and lessons learned from a trauma patient care protocolNehemiah T Liu, John B Holcomb, Charles E Wade, et al.
Journal of Critical Care|April 27, 2013
Development and validation of a novel fusion algorithm for continuous, accurate, and automated R-wave detection and calculation of signal-derived metricsNehemiah T Liu, Andriy I Batchinsky, Leopoldo C Cancio, et al.
Journal of Burn Care & Research : Official Publication of the American Burn Association|May 15, 2018
Predicting the Ability of Wounds to Heal Given Any Burn Size and Fluid Volume: An Analytical ApproachNehemiah T Liu, Julie A Rizzo, Beth A Shields, et al.
Burns : Journal of the International Society for Burn Injuries|December 15, 2019
Quantifying the effects of wound healing risk and potential on clinical measurements and outcomes of severely burned patients: A data-driven approachNehemiah T Liu, Sarah K Shingleton, Craig A Fenrich, et al.
The Journal of Trauma and Acute Care Surgery|April 29, 2017
The impact of patient weight on burn resuscitationNehemiah T Liu, Craig A Fenrich, Maria L Serio-Melvin, et al.
Medical & Biological Engineering & Computing|November 23, 2013
Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patientsNehemiah T Liu, John B Holcomb, Charles E Wade, et al.
Journal of Burn Care & Research : Official Publication of the American Burn Association|June 25, 2019
Relationship Between Burn Wound Location and Outcomes in Severely Burned Patients: More Than Meets the SizeNehemiah T Liu, Julie A Rizzo, Sarah K Shingleton, et al.
Journal of Burn Care & Research : Official Publication of the American Burn Association|October 15, 2021
Initial Results of the American Burn Association Observational Multicenter Evaluation on the Effectiveness of the Burn NavigatorJulie A Rizzo, Nehemiah T Liu, Elsa C Coates, et al.
Pageof 3