Weimer, James
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Publication OpenICE-lite: Towards a Connectivity Platform for the Internet of Medical Things(2018-05-01) Ivanov, Radoslav; Nguyen, Hung; Weimer, James; Sokolsky, Oleg; Lee, InsupThe Internet of Medical Things (IoMT) is poised to revolutionize medicine. However, medical device communication, coordination, and interoperability present challenges for IoMT applications due to safety, security, and privacy concerns. These challenges can be addressed by developing an open platform for IoMT that can provide guarantees on safety, security and privacy. As a first step, we introduce OpenICE-lite, a middleware for medical device interoperability that also provides security guarantees and allows other IoMT applications to view/analyze the data in real time. We describe two applications that currently utilize OpenICE-lite, namely (i) a critical pulmonary shunt predictor for infants during surgery; (ii) a remote pulmonary monitoring systems (RePulmo). Implementations of both systems are utilized by the Children’s Hospital of Philadelphia (CHOP) as quality improvements to patient care.Publication Willingness to Use a Wearable Device Capable of Detecting and Reversing Overdose Among People Who Use Opioids in Philadelphia(2021-07-01) Kanter, Katie; Gallagher, Ryan; Eweje, Feyisope; Lee, Alexander; Gordon, David; Landy, Stephen; Gasior, Julia; Soto-Calderon, Haideliza; Cronholm, Peter F; Weimer, James; Cocchiaro, Ben; Roth, Alexis; Brenner, Jacob; Lankenau, StephenBackground: The incidence of opioid-related overdose deaths has been rising for 30 years and has been further exacerbated amidst the COVID-19 pandemic. Naloxone can reverse opioid overdose, lower death rates, and enable a transition to medication for opioid use disorder. Though current formulations for community use of naloxone have been shown to be safe and effective public health interventions, they rely on bystander presence. We sought to understand the preferences and minimum necessary conditions for wearing a device capable of sensing and reversing opioid overdose among people who regularly use opioids. Methods: We conducted a combined cross-sectional survey and semi-structured interview at a respite center, shelter, and syringe exchange drop-in program in Philadelphia, Pennsylvania, USA during the COVID-19 pandemic in August and September 2020. The primary aim was to explore the proportion of participants who would use a wearable device to detect and reverse overdose. Preferences regarding designs and functionalities were collected via a questionnaire with items having Likert-based response options and a semi-structured interview intended to elicit feedback on prototype designs. Independent variables included demographics, opioid use habits, and previous experience with overdose. Results: A total of 97 adults with an opioid-use history of at least 3 months were interviewed. A majority of survey participants (76%) reported a willingness to use a device capable of detecting an overdose and automatically administering a reversal agent upon initial survey. When reflecting on the prototype, most respondents (75.5%) reported that they would wear the device always or most of the time. Respondents indicated discreetness and comfort as important factors that increased their chance of uptake. Respondents suggested that people experiencing homelessness and those with low tolerance for opioids would be in greatest need of the device. Conclusions: The majority of people sampled with a history of opioid use in an urban setting were interested in having access to a device capable of detecting and reversing an opioid overdose. Participants emphasized privacy and comfort as the most important factors influencing their willingness to use such a device. Trial Registration: NCT04530591Publication Characterizing Glycemic Control and Sleep in Adults with Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness Initiating Hybrid Closed Loop Insulin Delivery(2021-02-01) Kohl Malone, Susan; Peleckis, Amy J.; Jang, Sooyong; Grunin, Laura; Weimer, James; Yu, Gary; Lee, Insup; Rickels, Michael R.; Goel, NamniNocturnal hypoglycemia is life threatening for individuals with type 1 diabetes (T1D) due to loss of hypoglycemia symptom recognition (hypoglycemia unawareness) and impaired glucose counter regulation. These individuals also show disturbed sleep, which may result from glycemic dysregulation. Whether use of a hybrid closed loop (HCL) insulin delivery system with integrated continuous glucose monitoring (CGM) designed for improving glycemic control, relates to better sleep across time in this population remains unknown. The purpose of this study was to describe long-term changes in glycemic control and objective sleep after initiating hybrid closed loop (HCL) insulin delivery in adults with type 1 diabetes and hypoglycemia unawareness. To accomplish this, six adults (median age = 58 y) participated in an 18-month ongoing trial assessing HCL effectiveness. Glycemic control and sleep were measured using continuous glucose monitoring and wrist accelerometers every 3 months. Paired sample t-tests and Cohen’s d effect sizes modeled glycemic and sleep changes and the magnitude of these changes from baseline to 9 months. Reduced hypoglycemia (d = 0:47‐0:79), reduced basal insulin requirements (d = 0:48), and a smaller glucose coefficient of variation (d = 0:47) occurred with medium-large effect sizes from baseline to 9 months. Hypoglycemia awareness improved from baseline to 6 months with medium-large effect sizes (Clarke score (d = 0:60), lability index (d = 0:50), HYPO score (d = 1:06)). Shorter sleep onset latency (d = 1:53; p < 0:01), shorter sleep duration (d = 0:79), fewer total activity counts (d = 1:32), shorter average awakening length (d = 0:46), and delays in sleep onset (d = 1:06) and sleep midpoint (d = 0:72) occurred with medium-large effect sizes from baseline to 9 months. HCL led to clinically significant reductions in hypoglycemia and improved hypoglycemia awareness. Sleep showed a delayed onset, reduced awakening length and onset latency, and maintenance of high sleep efficiency after initiating HCL. Our findings add to the limited evidence on the relationships between diabetes therapeutic technologies and sleep health. This trial is registered with ClinicalTrials.gov (NCT03215914).Publication RePulmo: A Remote Pulmonary Monitoring System(2018-04-01) Nguyen, Hung; Ivanov, Radoslav; DeMauro, Sara B.; Weimer, JamesRemote physiological monitoring is increasing in popularity with the evolution of technologies in the healthcare industry. However, the current solutions for remote monitoring of blood-oxygen saturation, one of the most common continuously monitored vital signs, either have inconsistent accuracy or are not secure for transmitting over the network. In this paper, we propose RePulmo, an open-source platform for secure and accurate remote pulmonary data monitoring. RePulmo satisfies both robustness and security requirements by utilizing hospital-grade pulse oximeter devices with multiple layers of security enforcement. We describe two applications of RePulmo, namely (1) a remote pulmonary monitoring system for infants to support the Children’s Hospital of Philadelphia (CHOP) clinical trial; (2) a proof-of-concept of a low SpO2 smart alarm system.Publication Parameter-Invariant Monitor Design for Cyber Physical Systems(2018-01-01) Weimer, James; Ivanov, Radoslav; Chen, Sanjian; Roederer, Alexander; Sokolsky, Oleg; Lee, InsupThe tight interaction between information technology and the physical world inherent in Cyber-Physical Systems (CPS) can challenge traditional approaches for monitoring safety and security. Data collected for robust CPS monitoring is often sparse and may lack rich training data describing critical events/attacks. Moreover, CPS often operate in diverse environments that can have significant inter/intra-system variability. Furthermore, CPS monitors that are not robust to data sparsity and inter/intra-system variability may result in inconsistent performance and may not be trusted for monitoring safety and security. Towards overcoming these challenges, this paper presents recent work on the design of parameter-invariant (PAIN) monitors for CPS. PAIN monitors are designed such that unknown events and system variability minimally affect the monitor performance. This work describes how PAIN designs can achieve a constant false alarm rate (CFAR) in the presence of data sparsity and intra/inter system variance in real-world CPS. To demonstrate the design of PAIN monitors for safety monitoring in CPS with different types of dynamics, we consider systems with networked dynamics, linear-time invariant dynamics, and hybrid dynamics that are discussed through case studies for building actuator fault detection, meal detection in type I diabetes, and detecting hypoxia caused by pulmonary shunts in infants. In all applications, the PAIN monitor is shown to have (significantly) less variance in monitoring performance and (often) outperforms other competing approaches in the literature. Finally, an initial application of PAIN monitoring for CPS security is presented along with challenges and research directions for future security monitoring deployments.Publication Reducing Pulse Oximetry False Alarms Without Missing Life-Threatening Events(2018-09-01) Nguyen, Hung; Jang, Sooyong; Ivanov, Radoslav; Bonafide, Christopher P.; Weimer, James; Lee, InsupAlarm fatigue has been increasingly recognized as one of the most significant problems in the hospital environment. One of the major causes is the excessive number of false physiologic monitor alarms. An underlying problem is the inefficient traditional threshold alarm system for physiologic parameters such as low blood oxygen saturation (SpO2). In this paper, we propose a robust classification procedure based on the AdaBoost algorithm with reject option that can identify and silence false SpO2 alarms, while ensuring zero misclassified clinically significant alarms. Alarms and vital signs related to SpO2 such as heart rate and pulse rate, within monitoring interval are extracted into different numerical features for the classifier. We propose a variant of AdaBoost with reject option by allowing a third decision (i.e., reject) expressing doubt. Weighted outputs of each weak classifier are input to a softmax function optimizing to satisfy a desired false negative rate upper bound while minimizing false positive rate and indecision rate. We evaluate the proposed classifier using a dataset collected from 100 hospitalized children at Children's Hospital of Philadelphia and show that the classifier can silence 23.12% of false SpO2 alarms without missing any clinically significant alarms.