The Experts below are selected from a list of 15057 Experts worldwide ranked by ideXlab platform
Paul J Curry - One of the best experts on this subject based on the ideXlab platform.
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patterns of unexpected in hospital deaths a root cause analysis
Patient Safety in Surgery, 2011Co-Authors: Lawrence A Lynn, Paul J CurryAbstract:Background Respiratory Alarm Monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold Alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected.
Lawrence A Lynn - One of the best experts on this subject based on the ideXlab platform.
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patterns of unexpected in hospital deaths a root cause analysis
Patient Safety in Surgery, 2011Co-Authors: Lawrence A Lynn, Paul J CurryAbstract:Background Respiratory Alarm Monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold Alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected.
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Patterns of unexpected in-hospital deaths: a root cause analysis
Patient Safety in Surgery, 2011Co-Authors: Lawrence A Lynn, J Paul CurryAbstract:Background Respiratory Alarm Monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold Alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected. Methods The Pubmed database was searched for articles relating to methods for triggering rapid response teams and respiratory Alarms and these were contrasted with the fundamental timed pathophysiologic patterns of death which evolve due to sepsis, congestive heart failure, pulmonary embolism, hypoventilation, narcotic overdose, and sleep apnea. Results In contrast to the simplicity of the numeric threshold breach method of generating alerts, the actual patterns of evolving death are complex and do not share common features until near death. On hospital general floors, unexpected clinical instability leading to death often progresses along three distinct patterns which can be designated as Types I, II and III. Type I is a pattern comprised of hyperventilation compensated respiratory failure typical of congestive heart failure and sepsis. Here, early hyperventilation and respiratory alkalosis can conceal the onset of instability. Type II is the pattern of classic CO2 narcosis. Type III occurs only during sleep and is a pattern of ventilation and SPO2 cycling caused by instability of ventilation and/or upper airway control followed by precipitous and fatal oxygen desaturation if arousal failure is induced by narcotics and/or sedation. Conclusion The traditional threshold breach method of detecting instability on hospital wards was not scientifically derived; explaining the failure of threshold based Monitoring and rapid response team activation in randomized trials. Furthermore, the thresholds themselves are arbitrary and capricious. There are three common fundamental pathophysiologic patterns of unexpected hospital death. These patterns are too complex for early detection by any unifying numeric threshold. New methods and technologies which detect and identify the actual patterns of evolving death should be investigated.
J Paul Curry - One of the best experts on this subject based on the ideXlab platform.
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Patterns of unexpected in-hospital deaths: a root cause analysis
Patient Safety in Surgery, 2011Co-Authors: Lawrence A Lynn, J Paul CurryAbstract:Background Respiratory Alarm Monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold Alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected. Methods The Pubmed database was searched for articles relating to methods for triggering rapid response teams and respiratory Alarms and these were contrasted with the fundamental timed pathophysiologic patterns of death which evolve due to sepsis, congestive heart failure, pulmonary embolism, hypoventilation, narcotic overdose, and sleep apnea. Results In contrast to the simplicity of the numeric threshold breach method of generating alerts, the actual patterns of evolving death are complex and do not share common features until near death. On hospital general floors, unexpected clinical instability leading to death often progresses along three distinct patterns which can be designated as Types I, II and III. Type I is a pattern comprised of hyperventilation compensated respiratory failure typical of congestive heart failure and sepsis. Here, early hyperventilation and respiratory alkalosis can conceal the onset of instability. Type II is the pattern of classic CO2 narcosis. Type III occurs only during sleep and is a pattern of ventilation and SPO2 cycling caused by instability of ventilation and/or upper airway control followed by precipitous and fatal oxygen desaturation if arousal failure is induced by narcotics and/or sedation. Conclusion The traditional threshold breach method of detecting instability on hospital wards was not scientifically derived; explaining the failure of threshold based Monitoring and rapid response team activation in randomized trials. Furthermore, the thresholds themselves are arbitrary and capricious. There are three common fundamental pathophysiologic patterns of unexpected hospital death. These patterns are too complex for early detection by any unifying numeric threshold. New methods and technologies which detect and identify the actual patterns of evolving death should be investigated.
Luis M De Campos - One of the best experts on this subject based on the ideXlab platform.
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searching for bayesian network structures in the space of restricted acyclic partially directed graphs
Journal of Artificial Intelligence Research, 2003Co-Authors: Silvia Acid, Luis M De CamposAbstract:Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.
Sirish L. Shah - One of the best experts on this subject based on the ideXlab platform.
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Qualitative Fault Detection and Hazard Analysis Based on Signed Directed Graphs for Large-Scale Complex Systems
'IntechOpen', 2021Co-Authors: Fan Yang, Deyun Xiao, Sirish L. ShahAbstract:In this chapter, after the introduction of the SDG concept and modeling methods, the inference approaches aiming at the fault detection and hazard analysis, especially the SDG description of control systems, have been analyzed from theory to practice. The classical control methods on the basis of feedback idea are in common use, so the modeling and analysis of the systems under these control methods, have been discussed. When a control system is transformed into an SDG model, the direction of fault propagation in steady state may differ from the direction in initial response because of the control action. For a single-loop control system, the SDG is a directed path whose backbone is set point → measurement value → controlled variable → manipulated variable → controller output, which is also the fault propagation path and does not compose a loop. Based on this result, SDGs and fault propagation paths of various control systems can be obtained by the combination and connection of several single-loop control elements. Thus we do not have to list all the system equations when analyzing the actual problem, but only need to construct the local SDG for each separate control component and then combine them together, which is convenient for actual use. After analyzing the fault propagation paths in control systems, we can embed the resulted SDGs for initial response and final response into the SDG of the whole system and analyze the propagation paths considering the truncated or changed paths. This method enables the application of SDG method in large-scale complex systems. It is to be noted that model description should meet the actual needs but does not need to be too accurate. For example, the SDG-based qualitative analysis of the three-element control of the boiler water level usually do not refer to the details, so usually we only construct a single loop to describe the major problem and ignore the minor ones. In large-scale complex systems, however, SDG models can be adopted to describe the interactions between different parts and reveal the propagation for the use of fault analysis; it is the advantage of SDG models. In industrial systems, Alarm Monitoring design is a very important issue, among which the trade-off between missed Alarms and false Alarms should be treated appropriately. We should pay attention to two levels of design problems: In the local level, the threshold selection, data filtering and Alarm triggering are the key problems to be solved. In the system level, topology expression and sensor location for Alarm rationalization is essential. In this chapter, we have described and solved the sensor location problem aiming at the trade-off with the help of topology expressed by SDG. The optimization objective is expressed as the minimization of all the fault undetectabilities in the system. The false Alarm rate is used as constraint as well as the cost limit. Our future work may include: standard modeling method using XML-based process knowledge, modeling and validation of SDGs using process data, and combination of qualitative fault propagation and quantitative diagnosis
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Design of visualization plots of industrial Alarm and event data for enhanced Alarm management
Control Engineering Practice, 2018Co-Authors: Ahmad W. Al-dabbagh, Tongwen Chen, Sirish L. ShahAbstract:Abstract The availability of large volumes of Alarm & event data in complex industrial facilities has prompted the development of Alarm management techniques and also resulted in a great demand to transform such data and derived results into effective visual forms. Even though good visualization applications can be found in many existing studies, systematic studies to the design of visualization plots are still rare in the area of industrial Alarm Monitoring. More efforts need to be devoted to enriching the family of visualization techniques, so as to help industrial practitioners in better understanding the behavior of Alarm systems and to facilitate decision making for the enhancement of Alarm management. This paper presents timely work in the design of visualization plots of Alarm & event data. First, a comprehensive literature survey is carried out to investigate existing visualization techniques, which are categorized into three classes based on the input information. Problems in the existing studies are summarized and design requirements for visual analytics are presented. Then, design studies on the development of visualization plots are presented in three categories, including visualization towards overall performance, visualization towards pattern insights, and visualization towards realtime applications. Examples are provided to demonstrate the effectiveness and utility of these visualization techniques.