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Dr. Erraguntla is an Associate Professor of Practice in the Industrial and Systems Engineering department of Texas A&M University and a faculty member at Center for Remote Health Technologies and Systems. His areas of expertise include data mining, healthcare analytics, logistics, supply chain management, lean healthcare, epidemiology, and evidence-based medicine. He has 25 years of research experience in the industry and 7 years of teaching and research experience at Texas A&M University. His industry experiences include Knowledge based systems, AT&T Big data center of excellence, and i2 Technologies. He was the principal investigator and co-investigator for QNRF, CDC, DOD, DHHS, NASA, DOT, and Defense Health Program (DHP) funded Phase I, II and III projects on epidemiology, healthcare analytics, donor hemovigilance, crowd sourced data collection, and healthcare supply chain optimization. Currently, at Texas A&M, he is focused on teaching graduate and undergraduate courses, mentoring students based on experience and insights gained from industrial applications, and on executing analytics focused research projects.
Strategic and tactical capacity planning are critical decisions faced by hospitals. While these problems have received significant attention, current queueing-based approaches do not address realistic healthcare constraints such as blocking, transient arrivals, transient capacity assignments, and surge capacities. A queueing methodology is developed to extend the analysis of these constructs. The methodology developed is generic for hospitals responding to demand surges during epidemics and pandemics such as the recent COVID-19, and in other application areas in manufacturing, supply chain management, and logistics. The medical staff and patient chairs in the emergency room, beds in the operating theater, ICU, and medical/surgical care units are used in patient treatment at a hospital. They can be considered as servers in a system, where capacity and operational policies affect performance measures such as patient throughput. The methodology develops the probabilities from which system performance measures can be estimated for a serial queueing network with blocking. Transient analysis is employed, due to the time varying nature of the patient arrival patterns. The methodology has the capability to analyze different interventions such as increasing and decreasing capacities, and ambulance diversion. In order to handle typical hospital sized problems that result in thousands of ordinary differential equations defining the system probabilities, a transient version of Kanban queueing network decomposition is developed along with procedures for dealing with the discontinuities that arise at capacity changes. Verification/validation is presented along with several scenarios that illustrate the potential application of this methodology in emergency hospital management.
Guy L. Curry; Hiram Moya; Madhav Erraguntla; Amarnath Banerjee. Transient queueing analysis for emergency hospital management. IISE Transactions on Healthcare Systems Engineering 2021, 1 -16.
AMA StyleGuy L. Curry, Hiram Moya, Madhav Erraguntla, Amarnath Banerjee. Transient queueing analysis for emergency hospital management. IISE Transactions on Healthcare Systems Engineering. 2021; ():1-16.
Chicago/Turabian StyleGuy L. Curry; Hiram Moya; Madhav Erraguntla; Amarnath Banerjee. 2021. "Transient queueing analysis for emergency hospital management." IISE Transactions on Healthcare Systems Engineering , no. : 1-16.
Background Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. Objective This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. Methods Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). Results This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies. Conclusions Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.
Darpit Dave; Madhav Erraguntla; Mark Lawley; Daniel DeSalvo; Balakrishna Haridas; Siripoom McKay; Chester Koh. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes 2021, 6, e26909 .
AMA StyleDarpit Dave, Madhav Erraguntla, Mark Lawley, Daniel DeSalvo, Balakrishna Haridas, Siripoom McKay, Chester Koh. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes. 2021; 6 (2):e26909.
Chicago/Turabian StyleDarpit Dave; Madhav Erraguntla; Mark Lawley; Daniel DeSalvo; Balakrishna Haridas; Siripoom McKay; Chester Koh. 2021. "Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study." JMIR Diabetes 6, no. 2: e26909.
Hazardous scenarios emerging from complex system where number of functions are large and corresponding function coupling are humongous, are very difficult if not impossible to identify humanly. Today’s complex systems generate a very large dataset every minute and dynamic nature of the generated data makes it difficult to track such couplings. The Functional Resonance Analysis Method (FRAM) got success in recent years to understand hazards emerging from function couplings in complex systems, however, challenges remain to estimate aggregated couplings appropriately without quantitative analysis. The current study developed a data-driven approach to quantify function couplings using lift confidence intervals of association rules. Later, association rules were merged to identify the paths leading to a potential hazardous scenario. The paths were presented graphically and equipped with quantified coupling information and capable of providing guidance to prevent the emerging hazard scenario. The approach has been demonstrated with a case study of a polymerization process in process industry for which function couplings are represented by a very large dataset.
Mengxi Yu; Madhav Erraguntla; Noor Quddus; Costas Kravaris. A data-driven approach of quantifying function couplings and identifying paths towards emerging hazards in complex systems. Process Safety and Environmental Protection 2021, 150, 464 -477.
AMA StyleMengxi Yu, Madhav Erraguntla, Noor Quddus, Costas Kravaris. A data-driven approach of quantifying function couplings and identifying paths towards emerging hazards in complex systems. Process Safety and Environmental Protection. 2021; 150 ():464-477.
Chicago/Turabian StyleMengxi Yu; Madhav Erraguntla; Noor Quddus; Costas Kravaris. 2021. "A data-driven approach of quantifying function couplings and identifying paths towards emerging hazards in complex systems." Process Safety and Environmental Protection 150, no. : 464-477.
BACKGROUND Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. OBJECTIVE This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. METHODS Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). RESULTS This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies. CONCLUSIONS Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.
Darpit Dave; Madhav Erraguntla; Mark Lawley; Daniel DeSalvo; Balakrishna Haridas; Siripoom McKay; Chester Koh. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study (Preprint). 2021, 1 .
AMA StyleDarpit Dave, Madhav Erraguntla, Mark Lawley, Daniel DeSalvo, Balakrishna Haridas, Siripoom McKay, Chester Koh. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study (Preprint). . 2021; ():1.
Chicago/Turabian StyleDarpit Dave; Madhav Erraguntla; Mark Lawley; Daniel DeSalvo; Balakrishna Haridas; Siripoom McKay; Chester Koh. 2021. "Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study (Preprint)." , no. : 1.
Fatigue is defined as “a loss of force-generating capacity” in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant’s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier’s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.
Lilia Aljihmani; Oussama Kerdjidj; Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors 2020, 20, 6897 .
AMA StyleLilia Aljihmani, Oussama Kerdjidj, Yibo Zhu, Ranjana K. Mehta, Madhav Erraguntla, Farzan Sasangohar, Khalid Qaraqe. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors. 2020; 20 (23):6897.
Chicago/Turabian StyleLilia Aljihmani; Oussama Kerdjidj; Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. 2020. "Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor." Sensors 20, no. 23: 6897.
Background Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. Objective This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. Methods A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. Results Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. Conclusions Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.
Elaine Lum; Yu Kuei Lin; Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study. JMIR Diabetes 2020, 5, e17890 .
AMA StyleElaine Lum, Yu Kuei Lin, Karim Zahed, Farzan Sasangohar, Ranjana Mehta, Madhav Erraguntla, Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study. JMIR Diabetes. 2020; 5 (2):e17890.
Chicago/Turabian StyleElaine Lum; Yu Kuei Lin; Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. 2020. "Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study." JMIR Diabetes 5, no. 2: e17890.
Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake. Results: The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified. Conclusions: Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.
Darpit Dave; Daniel J. DeSalvo; Balakrishna Haridas; Siripoom McKay; Akhil Shenoy; Chester J. Koh; Mark Lawley; Madhav Erraguntla. Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction. Journal of Diabetes Science and Technology 2020, 15, 842 -855.
AMA StyleDarpit Dave, Daniel J. DeSalvo, Balakrishna Haridas, Siripoom McKay, Akhil Shenoy, Chester J. Koh, Mark Lawley, Madhav Erraguntla. Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction. Journal of Diabetes Science and Technology. 2020; 15 (4):842-855.
Chicago/Turabian StyleDarpit Dave; Daniel J. DeSalvo; Balakrishna Haridas; Siripoom McKay; Akhil Shenoy; Chester J. Koh; Mark Lawley; Madhav Erraguntla. 2020. "Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction." Journal of Diabetes Science and Technology 15, no. 4: 842-855.
Neuromuscular fatigue affects workers’ productivity and health, which is further deteriorated with chronic conditions such as type 1 diabetes (T1D). Enhanced physiological tremor, a key indicator of neuromuscular fatigue, shows great potential in detecting the onset of neuromuscular fatigue. This study aims to determine the feasibility of using a cost-effective wearable accelerometer-based microelectromechanical sensor to convey neuromuscular fatigue-related tremor information in healthy and T1D adults. 42 adults (22 healthy, 20 T1D), equipped with a finger and a wrist accelerometer, performed intermittent submaximal isometric handgrip fatigue exercises using a grip dynamometer. Motor variability feature, namely, Coefficient of Variation (CV), and motor complexity feature, namely approximate entropy (ApEn), were extracted from the force signal of dynamometer and from the finger and wrist tremor accelerometry signals and subjected to statistical analysis. First, significant positive correlations were found between tremor accelerometry and force signal in terms of motor variability and complexity features. Second, a three-way (fatigue phase: early, middle, late; gender: male, female; condition: healthy, T1D) analysis of variance resulted in a significant fatigue effect on both accelerometry and force measurements in terms of motor variability and complexity features. Apart from finger CV, no other features showed any gender or condition effects. These findings indicate that finger and wrist tremors measured by accelerometer-based sensors can retain the robustness of fatiguerelated motor variability and complexity. Wrist tremor features were found to capture fatigue development across both healthy and diabetic males and females, thereby offering comparable fatigue detection and management in adults with chronic conditions.
Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults. IEEE Sensors Journal 2020, 20, 11183 -11190.
AMA StyleYibo Zhu, Ranjana K. Mehta, Madhav Erraguntla, Farzan Sasangohar, Khalid Qaraqe. Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults. IEEE Sensors Journal. 2020; 20 (19):11183-11190.
Chicago/Turabian StyleYibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. 2020. "Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults." IEEE Sensors Journal 20, no. 19: 11183-11190.
Major depressive disorder (MDD) has shown to negatively impact physical recovery in a variety of medical events (e.g., stroke and spinal cord injuries). Yet depression assessments, which are typically subjective in nature, are seldom considered to develop or guide rehabilitation strategies. The present study developed a predictive depression assessment technique using functional near-infrared spectroscopy (fNIRS) that can be rapidly integrated or performed concurrently with existing physical rehabilitation tasks. Thirty-one volunteers, including 14 adults clinically diagnosed with MDD and 17 healthy adults, participated in the study. Brain oxyhemodynamic (HbO) responses were recorded using a 16-channel wearable continuous-wave fNIRS device while the volunteers performed the Grasp and Release Test in four 16-minute blocks. Ten features, extracted from HbO signals, from each channel served as inputs to XGBoost and Random Forest algorithms developed for each block and combination of successive blocks. Top 5 common features resulted in a classification accuracy of 92.6%, sensitivity of 84.8%, and specificity of 91.7% using the XGBoost classifier. This study identified mean HbO, full width half maximum and kurtosis, as specific neuromarkers, for predicting MDD across specific depression-related regions of interests (i.e., dorsolateral and ventrolateral prefrontal cortex). Our results suggest that a wearable fNIRS head probe monitoring specific brain regions, and limiting extraction to few features, can enable quick setup and rapid assessment of depression in patients. The overarching goal is to embed predictive neurotechnology during post-stroke and post-spinalcord-injury rehabilitation sessions to monitor patients' depression symptomology so as to actively guide decisions about motor therapies.
Yibo Zhu; Jagadish K. Jayagopal; Ranjana K. Mehta; Madhav Erraguntla; Joseph Nuamah; Anthony D. McDonald; Heather Taylor; Shuo-Hsiu Chang. Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 961 -969.
AMA StyleYibo Zhu, Jagadish K. Jayagopal, Ranjana K. Mehta, Madhav Erraguntla, Joseph Nuamah, Anthony D. McDonald, Heather Taylor, Shuo-Hsiu Chang. Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (4):961-969.
Chicago/Turabian StyleYibo Zhu; Jagadish K. Jayagopal; Ranjana K. Mehta; Madhav Erraguntla; Joseph Nuamah; Anthony D. McDonald; Heather Taylor; Shuo-Hsiu Chang. 2020. "Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 4: 961-969.
BACKGROUND Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. OBJECTIVE This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. METHODS A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. RESULTS Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. CONCLUSIONS Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.
Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint). 2020, 1 .
AMA StyleKarim Zahed, Farzan Sasangohar, Ranjana Mehta, Madhav Erraguntla, Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint). . 2020; ():1.
Chicago/Turabian StyleKarim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. 2020. "Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint)." , no. : 1.
A common challenge with all opioid use disorder treatment paths is withdrawal management. When withdrawal symptoms are not effectively monitored and managed, they lead to relapse which often leads to deadly overdose. A prerequisite for effective opioid withdrawal management is early identification and assessment of withdrawal symptoms. The objective of this research was to describe the type and content of opioid withdrawal monitoring methods, including surveys, scales and technology, to identify gaps in research and practice that could inform the design and development of novel withdrawal management technologies. A scoping review of literature was conducted. PubMed, EMBASE and Google Scholar were searched using a combination of search terms. Withdrawal scales are the main method of assessing and quantifying opioid withdrawal intensity. The search yielded 18 different opioid withdrawal scales used within the last 80 years. While traditional opioid withdrawal scales for patient monitoring are commonly used, most scales rely heavily on patients’ self-report and frequent observations, and generally suffer from lack of consensus on the criteria used for evaluation, mode of administration, type of reporting (e.g., scales used), frequency of administration, and assessment window. It is timely to investigate how opioid withdrawal scales can be complemented or replaced with reliable monitoring technologies. Use of noninvasive wearable sensors to continuously monitor physiologic changes associated with opioid withdrawal represents a potential to extend monitoring outside clinical setting.
Joseph K. Nuamah; Farzan Sasangohar; Madhav Erraguntla; Ranjana K. Mehta. The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies. BMC Medical Informatics and Decision Making 2019, 19, 1 -11.
AMA StyleJoseph K. Nuamah, Farzan Sasangohar, Madhav Erraguntla, Ranjana K. Mehta. The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies. BMC Medical Informatics and Decision Making. 2019; 19 (1):1-11.
Chicago/Turabian StyleJoseph K. Nuamah; Farzan Sasangohar; Madhav Erraguntla; Ranjana K. Mehta. 2019. "The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies." BMC Medical Informatics and Decision Making 19, no. 1: 1-11.
Mosquito-borne pathogens continue to be a significant burden within human populations, with Aedes aegypti continuing to spread dengue, chikungunya, and Zika virus throughout the world. Using data from a previously conducted study, a linear regression model was constructed to predict the aquatic development rates based on the average temperature, temperature fluctuation range, and larval density. Additional experiments were conducted with different parameters of average temperature and larval density to validate the model. Using a paired t-test, the model predictions were compared to experimental data and showed that the prediction models were not significantly different for average pupation rate, adult emergence rate, and juvenile mortality rate. The models developed will be useful for modeling and estimating the upper limit of the number of Aedes aegypti in the environment under different temperature, diurnal temperature variations, and larval densities.
Josef Zapletal; Himanshu Gupta; Madhav Erraguntla; Zach Adelman; Kevin M. Myles; Mark A. Lawley. Predicting aquatic development and mortality rates of Aedes aegypti. PLOS ONE 2019, 14, e0217199 .
AMA StyleJosef Zapletal, Himanshu Gupta, Madhav Erraguntla, Zach Adelman, Kevin M. Myles, Mark A. Lawley. Predicting aquatic development and mortality rates of Aedes aegypti. PLOS ONE. 2019; 14 (5):e0217199.
Chicago/Turabian StyleJosef Zapletal; Himanshu Gupta; Madhav Erraguntla; Zach Adelman; Kevin M. Myles; Mark A. Lawley. 2019. "Predicting aquatic development and mortality rates of Aedes aegypti." PLOS ONE 14, no. 5: e0217199.
About 425 million adults around the world were living with diabetes in 2017. A relevant condition called Hypoglycemia is characterized by a dangerous low level of blood sugar that could be fatal to diabetic patients. Continuous glucose monitoring systems (CGMS) are the most popular commercially available technology for detecting diabetic hypoglycemia. However, CGMSs are invasive, costly, and not user-centric thereby not sustainable for diabetes management. This paper documents our initial efforts in designing an inexpensive, non-invasive, wearable physiological tremor sensory system to detect the onset of hypoglycemic events of diabetic patients. The design cycle briefly presented here includes: 1) determination of system (technology and user) requirements, 2) development of the tremor detection prototype, and 3) testing and validation of the system in non-clinical and clinical settings using human factors, data analytics, and biomedical sciences techniques and approaches.
Yibo Zhu; Karim Zahed; Ranjana K. Mehta; Farzan Sasangohar; Madhav Erraguntla; Mark Lawley; Hasan Tahir Abbas; Khaled Qaraqe. Non-invasive Wearable System for Hypoglycemia Detection: A Proof of Concept User-Centered Design Process. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2018, 62, 1052 -1056.
AMA StyleYibo Zhu, Karim Zahed, Ranjana K. Mehta, Farzan Sasangohar, Madhav Erraguntla, Mark Lawley, Hasan Tahir Abbas, Khaled Qaraqe. Non-invasive Wearable System for Hypoglycemia Detection: A Proof of Concept User-Centered Design Process. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2018; 62 (1):1052-1056.
Chicago/Turabian StyleYibo Zhu; Karim Zahed; Ranjana K. Mehta; Farzan Sasangohar; Madhav Erraguntla; Mark Lawley; Hasan Tahir Abbas; Khaled Qaraqe. 2018. "Non-invasive Wearable System for Hypoglycemia Detection: A Proof of Concept User-Centered Design Process." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1: 1052-1056.
Diabetes is a prevalent condition affecting millions of patients globally. Some diabetic patients suffer from a deadly condition called Hypoglycemia (sudden drop in blood glucose levels). Continuous Glucose Monitors (CGMs) have been the most pervasive tool used to track blood glucose levels but these tools are invasive and costly. While early detection of hypoglycemia has been studied, current approaches do not leverage tremors; which are a primary symptom of hypoglycemia. A scoping review was conducted to understand the relationship between tremors and hypoglycemia, and to document any efforts that utilized tremor signatures non-invasively to detect hypoglycemic events. Findings suggest that hypoglycemic tremors are a medium frequency tremor, more resistant to hypoglycemic impairment than other symptoms, and have not been fully explored yet. This paper also documents the work in progress to utilize a novel wearable device that predicts the onsets of hypoglycemia using hand tremor sensing.
Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Mark Lawley; Khalid Qaraqe. Investigating the Efficacy of Using Hand Tremors for Early Detection of Hypoglycemic Events: A Scoping Literature Review. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2018, 62, 1211 -1215.
AMA StyleKarim Zahed, Farzan Sasangohar, Ranjana Mehta, Madhav Erraguntla, Mark Lawley, Khalid Qaraqe. Investigating the Efficacy of Using Hand Tremors for Early Detection of Hypoglycemic Events: A Scoping Literature Review. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2018; 62 (1):1211-1215.
Chicago/Turabian StyleKarim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Mark Lawley; Khalid Qaraqe. 2018. "Investigating the Efficacy of Using Hand Tremors for Early Detection of Hypoglycemic Events: A Scoping Literature Review." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1: 1211-1215.
Mosquito-borne pathogens continue to be a significant burden within human populations, with Aedes aegypti continuing to spread dengue, chikungunya, and Zika virus throughout the world. Using data from a previously conducted study, a linear regression model was constructed to predict the aquatic development rates based on the average temperature, temperature fluctuation range, and larval density. Additional experiments were conducted with different parameters of average temperature and larval density to validate the model. Using a paired t-test, the model predictions were compared to experimental data and showed that the prediction models were not significantly different for average pupation rate, adult emergence rate, and juvenile mortality rate. The models developed will be useful for modeling and estimating the number of Aedes aegypti in the environment under different temperature, diurnal temperature variations, and larval densities.Author SummaryUsing experimental data from experiments conducted on Aedes aegypti, we formulated regression models to predict pupation, adult emergence, and juvenile mortality rates based on average temperature, temperature fluctuation range, and larval density. The prediction models produced were shown to account for high levels of variation within the data. Validation was performed by comparing omitted data sets to the predictions generated by our models. Our results show that the models produce results that are not significantly different from the experimental results and are capable of predicting aquatic development rates of Ae. aegypti.
Josef Zapletal; Himanshu Gupta; Madhav Erraguntla; Zach N. Adelman; Kevin M. Myles; Mark A. Lawley. Predicting Aquatic Development and Mortality Rates of Aedes Aegypti. 2018, 407502 .
AMA StyleJosef Zapletal, Himanshu Gupta, Madhav Erraguntla, Zach N. Adelman, Kevin M. Myles, Mark A. Lawley. Predicting Aquatic Development and Mortality Rates of Aedes Aegypti. . 2018; ():407502.
Chicago/Turabian StyleJosef Zapletal; Himanshu Gupta; Madhav Erraguntla; Zach N. Adelman; Kevin M. Myles; Mark A. Lawley. 2018. "Predicting Aquatic Development and Mortality Rates of Aedes Aegypti." , no. : 407502.
The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.
Madhav Erraguntla; Josef Zapletal; Mark Lawley. Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management. Health Informatics Journal 2017, 25, 1170 -1187.
AMA StyleMadhav Erraguntla, Josef Zapletal, Mark Lawley. Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management. Health Informatics Journal. 2017; 25 (4):1170-1187.
Chicago/Turabian StyleMadhav Erraguntla; Josef Zapletal; Mark Lawley. 2017. "Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management." Health Informatics Journal 25, no. 4: 1170-1187.
A scoping literature review was conducted to summarize the current research trends in fatigue identification with applications to human activity recognition through the use of diverse commercially available accelerometers. This paper also provides a brief overview of heart rate variability and its effect on fatigue. The linkage between recognizing an individual’s unique physical activities, and its possible feedback to manage fatigue levels were explored. Overall, triangulation of heart rate variability and accelerometer data show promise in identify chronic cognitive and physical fatigue levels.
Karla Gonzalez; Farzan Sasangohar; Ranjana K. Mehta; Mark Lawley; Madhav Erraguntla. Measuring Fatigue through Heart Rate Variability and Activity Recognition: A Scoping Literature Review of Machine Learning Techniques. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2017, 61, 1748 -1752.
AMA StyleKarla Gonzalez, Farzan Sasangohar, Ranjana K. Mehta, Mark Lawley, Madhav Erraguntla. Measuring Fatigue through Heart Rate Variability and Activity Recognition: A Scoping Literature Review of Machine Learning Techniques. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2017; 61 (1):1748-1752.
Chicago/Turabian StyleKarla Gonzalez; Farzan Sasangohar; Ranjana K. Mehta; Mark Lawley; Madhav Erraguntla. 2017. "Measuring Fatigue through Heart Rate Variability and Activity Recognition: A Scoping Literature Review of Machine Learning Techniques." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1: 1748-1752.