Nehemiah T. Liu

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Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of(More)
BACKGROUND Acute respiratory distress syndrome (ARDS) prevalence and related outcomes in burned military casualties from Iraq and Afghanistan have not been described previously. The objective of this article was to report ARDS prevalence and its associated in-hospital mortality in military burn patients. METHODS Demographic and physiologic data were(More)
PURPOSE Previous studies have shown that heart rate complexity may be a useful indicator of patient status in the critical care environment but will require continuous, accurate, and automated R-wave detection (RWD) in the electrocardiogram (ECG). Although numerous RWD algorithms exist, accurate detection remains a challenge. The purpose of this study was(More)
To date, no studies have attempted to utilize data from a combination of vital signs, heart rate variability and complexity (HRV, HRC), as well as machine learning (ML), for identifying the need for lifesaving interventions (LSIs) in trauma patients. The objectives of this study were to examine the utility of the above for identifying LSI needs and compare(More)
This study focused on the impact of noise on the reliability of heart-rate variability and complexity (HRV, HRC) to discriminate between different trauma patients and to monitor individual patients. Life-saving interventions (LSIs) were chosen as an endpoint because performance of LSIs is a critical aspect of trauma patient care. Noise was modeled and(More)
BACKGROUND This study aimed to determine the effectiveness of using a wireless, portable vital signs monitor (WVSM) for predicting the need for lifesaving interventions (LSIs) in the emergency department (ED) and use a multivariate logistic regression model to determine whether the WVSM was an improved predictor of LSIs in the ED over the standard of care(More)
Heart-rate complexity (HRC) has been proposed as a new vital sign for critical care medicine. The purpose of this research was to develop a reliable method for determining HRC continuously in real time in critically ill patients using multiple waveform channels that also compensates for noisy and unreliable data. Using simultaneously acquired(More)
This study was designed to investigate the quality of data in the pre-hospital and emergency departments when using a wearable vital signs monitor and examine the efficacy of a combined model of standard vital signs and respective data quality indices (DQIs) for predicting the need for life-saving interventions (LSIs) in trauma patients. It was hypothesised(More)
BACKGROUND This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional(More)
The goal of this study was to determine the effectiveness of using traditional and new vital signs (heart rate variability and complexity [HRV, HRC]) for predicting mortality and the need for life-saving interventions (LSIs) in prehospital trauma patients. Our hypothesis was that statistical regression models using traditional and new vital signs would be(More)