2,451 research outputs found

    User-centered visual analysis using a hybrid reasoning architecture for intensive care units

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    One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

    Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

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    Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced

    An investigation into the effects of commencing haemodialysis in the critically ill

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    <b>Introduction:</b> We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3 hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared to subsequent ones. <b>Methods:</b> Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology (HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable, then the nature of instability was examined by recording whether changes crossed defined physiological ranges. The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful, or beneficial/potentially beneficial. <b>Results:</b> Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%. Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes. This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s exact test gives a result of p=0.68, reinforcing the lack of significant variance. <b>Conclusions:</b> Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are beneficial in nature

    Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

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    Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.Comment: 10 pages, 7 figures, 2 table

    Hospital Variation In Admission To Intensive Care Units For Patients With Myocardial Infarction

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    The treatment of patients with myocardial infarction was transformed by the introduction of intensive care units (ICUs), but we know little about how contemporary hospitals employ this resource-intensive setting and whether higher use is associated with better outcomes. We sought to determine the variation in the rates of ICU admission across hospitals for patients with myocardial infarction and whether these rates were associated with mortality or usage of critical care therapies. We hypothesized that large variations exist in rates of ICU use for these patients across hospitals, but that these differences would not be associated with in-hospital mortality. We identified 114,980 adult hospitalizations for acute myocardial infarction from 311 hospitals in the 2009-10 Premier database using codes from the International Classification of Diseases, Ninth Revision, Clinical Modification. Hospitals were stratified into quartiles by rates of ICU admission for patients with myocardial infarction. Across quartiles, we examined in-hospital risk-standardized mortality rates and usage rates of critical care therapies for these patients. Rates of ICU admission for patients with myocardial infarction varied markedly among hospitals (median 48%, IQR 35%-61%, range 0%-98%) and there was no association with in-hospital risk-standardized mortality rates (6% all quartiles; p=0.7). However, hospitals admitting more patients to the ICU were more likely to use critical care therapies overall (mechanical ventilation [from Quartile 1 with lowest rate of ICU use to Quartile 4 with highest rate: 13% to 16%], vasopressors/inotropes [17% to 21%], intra-aortic balloon pumps [4% to 7%], and pulmonary artery catheters [4% to 5%]; p for trend\u3c0.05 in all comparisons). Rates of ICU admission for myocardial infarction vary substantially across hospitals and were not associated with differences in mortality, but were associated with greater use of critical care therapies

    Device for measuring bronchodilator delivery and response in resource-limited settings

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