Roederer, Alexander

Email Address
Research Projects
Organizational Units
Research Interests

Search Results

Now showing 1 - 6 of 6
  • Publication
    GSA: A Framework for Rapid Prototyping of Smart Alarm Systems
    (2010-11-11) King, Andrew; Roederer, Alex; Arney, David; Chen, Sanjian; Fortino-Mullen, Margaret; Giannareas, Ana; Hanson III, C. William; Kern, Vanessa; Stevens, Nicholas; Viesca Trevino, Adrian; Park, Soojin; Sokolsky, Oleg; Lee, Insup; Tannen, Jonathan
    We describe the Generic Smart Alarm, an architectural framework for the development of decision support modules for a variety of clinical applications. The need to quickly process patient vital signs and detect patient health events arises in many clinical scenarios, from clinical decision support to tele-health systems to home-care applications. The events detected during monitoring can be used as caregiver alarms, as triggers for further downstream processing or logging, or as discrete inputs to decision support systems or physiological closed-loop applications. We believe that all of these scenarios are similar, and share a common framework of design. In attempting to solve a particular instance of the problem, that of device alarm fatigue due to numerous false alarms, we devised a modular system based around this framework. This modular design allows us to easily customize the framework to address the specific needs of the various applications, and at the same time enables us to perform checking of consistency of the system. In the paper we discuss potential specific clinical applications of a generic smart alarm framework, present the proposed architecture of such a framework, and motivate the benefits of a generic framework for the development of new smart alarm or clinical decision support systems.
  • Publication
    Parameter-Invariant Design of Medical Alarms
    (2015-10-01) Weimer, James; Ivanov, Radoslav; Roederer, Alexander; Chen, Sanjian; Lee, Insup
    The recent explosion of low-power low-cost communication, sensing, and actuation technologies has ignited the automation of medical diagnostics and care in the form of medical cyber physical systems (MCPS). MCPS are poised to revolutionize patient care by providing smarter alarm systems, clinical decision support, advanced diagnostics, minimally invasive surgical care, improved patient drug delivery, and safety and performance guarantees. With the MCPS revolution emerges a new era in medical alarm systems, where measurements gathered via multiple devices are fused to provide early detection of critical conditions. The alarms generated by these next generation monitors can be exploited by MCPS to further improve performance, reliability, and safety. Currently, there exist several approaches to designing medical monitors ranging from simple sensor thresholding techniques to more complex machine learning approaches. While all the current design approaches have different strengths and weaknesses, their performance degrades when underlying models contain unknown parameters and training data is scarce. Under this scenario, an alternative approach that performs well is the parameter-invariant detector, which utilizes sufficient statistics that are invariant to unknown parameters to achieve a constant false alarm rate across different systems. Parameter-invariant detectors have been successfully applied in other cyber physical systems (CPS) applications with structured dynamics and unknown parameters such as networked systems, smart buildings, and smart grids; most recently, the parameter-invariant approach has been recently extended to medical alarms in the form of a critical shunt detector for infants undergoing a lung lobectomy. The clinical success of this case study application of the parameter-invariant approach is paving the way for a range of other medical monitors. In this tutorial, we present a design methodology for medical parameter-invariant monitors. We begin by providing a motivational review of currently employed medical alarm techniques, followed by the introduction of the parameter-invariant design approach. Finally, we present a case study example to demonstrate the design of a parameter-invariant alarm for critical shunt detection in infants during surgical procedures.
  • Publication
    Clinician-in-the-Loop Annotation of ICU Bedside Alarm Data
    (2016-06-01) Roederer, Alexander; Dimartino, Joseph; Gutsche, Jacob; Hanson III, C. William; Lee, Insup; Mullen-Fortino, Margaret; Shah, Sachin
    In this work, we describe the state of clinical monitoring in the intensive care unit and operating room, where patients are at their most fragile and thus monitoring is most heightened. We describe how large amounts of data generated by monitoring patients’ physiologic signals, along with the ubiquitous aspecific threshold alarms in use today, cause dangerous alarm fatigue for medical caregivers. In order to build more specific, more useful alarms, we gathered a novel data set that would allow us to assess the number, types, and utility of alarms currently in use in the intensive care unit. To do this, we developed a system to collect physiologic monitor data, alarms, and annotations of those alarms provided electronically by clinicians. We describe the collection process for this novel data set and provide a preliminary description of the data.
  • Publication
    Robust Monitoring of Hypovolemia in Intensive Care Patients Using Photoplethysmogram Signals
    (2015-08-01) Roederer, Alexander; Weimer, James; Dimartino, Joseph; Gutsche, Jacob; Lee, Insup
    The paper presents a fingertip photoplethysmography based technique to assess patient fluid status that is robust to waveform artifacts and health variability in the underlying patient population. The technique is intended for use in intensive care units, where patients are at risk for hypovolemia, and signal artifacts and inter-patient variations in health are common. Input signals are preprocessed to remove artifact, then a parameter-invariant statistic is calculated to remove effects of patient-specific physiology. Patient data from the Physionet MIMICII database was used to evaluate the performance of this technique. The proposed method was able to detect hypovolemia within 24 hours of onset in all hypovolemic patients tested, while producing minimal false alarms over non-hypovolemic patients.
  • Publication
    Clinical Decision Support for Integrated Cyber-Physical Systems: A Mixed Methods Approach
    (2012-01-01) Roederer, Alex; Hicks, Andrew R.; Oyeniran, Enny; Lee, Insup; Park, Soojin
    We describe the design and implementation of a clinical decision support system for assessing risk of cerebral vasospasm in patients who have been treated for aneurysmal subarachnoid hemorrhage. We illustrate the need for such clinical decision support systems in the intensive care environment, and propose a three pronged approach to constructing them, which we believe presents a balanced approach to patient modeling. We illustrate the data collection process, choice and development of models, system architecture, and methodology for user interface design. We close with a description of future work, a proposed evaluation mechanism, and a description of the demo to be presented.
  • Publication
    Towards Non-Invasive Monitoring of Hypovolemia in Intensive Care Patients
    (2015-04-13) Roederer, Alexander; Weimer, James; Dimartino, Joseph; Gutsche, Jacob; Lee, Insup
    Hypovolemia caused by internal hemorrhage is a major cause of death in critical care patients. However, hypovolemia is difficult to diagnose in a timely fashion, as obvious symptoms do not manifest until patients are already nearing a critical state of shock. Novel non-invasive methods for detecting hypovolemia in the literature utilize the photoplethysmogram (PPG) waveform generated by the pulse-oximeter attached to a finger or ear. Until now, PPG-based alarms have been evaluated only on healthy patients under ideal testing scenarios (e.g., motionless patients); however, the PPG is sensitive to patient health and significant artifacts manifest when patients move. Since patient health varies within the intensive care unit (ICU) and ICU patients typically do not remain motionless, this work introduces a PPG-based monitor designed to be robust to waveform artifacts and health variability in the underlying patient population. To demonstrate the promise of our approach, we evaluate the proposed monitor on a small sample of intensive care patients from the Physionet database. The monitor detects hypovolemia within a twelve hour window of nurse documentation of hypovolemia when it is present, and achieves a low false alarm rate over patients without documented hypovolemia.