1,721,200 research outputs found
Explainable AI Analysis of a Prediction Model for Detecting Premature Atrial and Ventricular Complexes
Full text linkA smart hospital-driven approach to precision pharmacovigilance
Full text linkResearchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using ‘smart hospitals’ as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions
Innovative approaches to collecting, aggregating, and analyzing adverse drug events in smart hospitals
Full text linkBACKGROUND: The increasing integration of electronic health records (EHRs) and their secondary use provide new pathways to advance drug safety. Smart hospitals use advanced data collection to enhance pharmacovigilance and better detect adverse drug events (ADEs). Finland’s secondary-use legislation embodies this data-sharing shift.
OBJECTIVE: This work synthesizes current evidence and proposes strategies to strengthen ADE detection and analysis in smart hospitals by integrating multimodal data sources, including EHRs, sensor data, and the Internet of Medical Things (IoMT), to raise overall drug safety standards.
METHODS: We review the Global Trigger Tool (GTT), sensor technologies, and IoMT for ADE detection and outline how these techniques can be combined, offering a more comprehensive approach to monitoring.
RESULTS: Integrating GTT, sensors, and IoMT into a unified system could improve ADE detection and prevention. Combining pharmacovigilance tools with advanced technology can increase the volume and quality of ADE data and supports a preventive focus on patient safety.
CONCLUSIONS: The study underscores the importance of the smart-hospital concept and emerging data-collection methods in pharmacovigilance. By adopting a holistic approach to ADE detection and integrating diverse data sources, more robust drug-safety surveillance and patient care can be achieved when coupled with human oversight and regulatory compliance
Automatic assessment of functional suppression of the central nervous system due to propofol anesthetic infusion : From EEG phenomena to a quantitative index
Full text linkAbstractThe rationale for automatically monitoring anesthetic drug effects on the central nervous system (CNS) is to improve possibilities to gain objective information on a patient’s state and to adjust the medication individually. Although monitors have shown their usefulness in practice, there are still a number of unclear issues, especially with respect to the scientific foundations and validity of CNS monitoring techniques, and in monitoring the light hypnotic levels. Current monitors are, for example, often based on heuristics and ad hoc solutions. However, a quantitative index for anesthetic drug effect should have a sound relationship with observations and with the selected control variable. The research objectives are: (1) to explore propofol anesthetic related neurophysiological phenomena that can be applied in the automatic assessment of CNS suppression; (2) to develop a valid control variable for this purpose; (3) by means of digital signal processing and mathematical modeling, to design and to evaluate the performance of an index that correlates with the control variable.This dissertation introduces potentially useful neurophysiological phenomena, such as changes in phase synchronization between different EEG channels due to anesthesia, and painful stimulus evoked responses during the burst suppression. Furthermore, it refines the progression of the time-frequency patterns during the induction of anesthesia and shows their relation to the instant of unresponsiveness. The presented spontaneous and evoked EEG phenomena provide complementary information about the CNS functional suppression. Most significantly, the dissertation proposes a continuous and observation based control variable (r scale) and the means to predict its values by using EEG data. The definition of the scale provides a basis for anticipating the instant of the loss of consciousness. Additionally, the phase synchronization index as an indicator of drug effect is introduced. The approximate entropy descriptor performance is evaluated and optimised with a non-stationary signal recorded during the induction of anesthesia.The results open up opportunities to improve the preciseness, scientific validity and the interpretation of information on the anesthetic effects on CNS, and therefore, to increase the reliability of the anesthesia monitoring. Further work is needed to extend and verify the results in deep anesthesia.Academic dissertation to be presented, with the assent of the Faculty of Technology of the University of Oulu, for public defence in Auditorium IT115, Linnanmaa, on September 29th, 2006, at 12 noonAbstract
The rationale for automatically monitoring anesthetic drug effects on the central nervous system (CNS) is to improve possibilities to gain objective information on a patient’s state and to adjust the medication individually. Although monitors have shown their usefulness in practice, there are still a number of unclear issues, especially with respect to the scientific foundations and validity of CNS monitoring techniques, and in monitoring the light hypnotic levels. Current monitors are, for example, often based on heuristics and ad hoc solutions. However, a quantitative index for anesthetic drug effect should have a sound relationship with observations and with the selected control variable. The research objectives are: (1) to explore propofol anesthetic related neurophysiological phenomena that can be applied in the automatic assessment of CNS suppression; (2) to develop a valid control variable for this purpose; (3) by means of digital signal processing and mathematical modeling, to design and to evaluate the performance of an index that correlates with the control variable.
This dissertation introduces potentially useful neurophysiological phenomena, such as changes in phase synchronization between different EEG channels due to anesthesia, and painful stimulus evoked responses during the burst suppression. Furthermore, it refines the progression of the time-frequency patterns during the induction of anesthesia and shows their relation to the instant of unresponsiveness. The presented spontaneous and evoked EEG phenomena provide complementary information about the CNS functional suppression.
Most significantly, the dissertation proposes a continuous and observation based control variable (r scale) and the means to predict its values by using EEG data. The definition of the scale provides a basis for anticipating the instant of the loss of consciousness. Additionally, the phase synchronization index as an indicator of drug effect is introduced. The approximate entropy descriptor performance is evaluated and optimised with a non-stationary signal recorded during the induction of anesthesia.
The results open up opportunities to improve the preciseness, scientific validity and the interpretation of information on the anesthetic effects on CNS, and therefore, to increase the reliability of the anesthesia monitoring. Further work is needed to extend and verify the results in deep anesthesia
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review
No full text: Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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