1,721,352 research outputs found
Leveraging artificial intelligence for enhanced and human-centered healthcare solutions
Full text linkArtificial Intelligence (AI) is increasingly recognized as a transformative force in healthcare, offering unprecedented opportunities to enhance disease diagnosis, management, and prevention. This PhD thesis is rooted in two fundamental research areas: the application of AI to health and epidemiological data for the purposes of disease prevention and monitoring, and the utilization of AI techniques for the analysis of bioelectrical signals to support clinical decision-making.
The first research area delves into the sophisticated analysis of extensive health and epidemiological datasets using cutting-edge machine learning (ML) methodologies. The objective is to uncover significant patterns that can inform and improve the prevention and management of chronic diseases. By identifying these patterns, the research enables the creation of personalized intervention strategies tailored to individual patient profiles, while also optimizing disease management on a broader, population-wide scale. This approach not only contributes to the advancement of public health but also sets the stage for more proactive healthcare practices.
The second research focus of this thesis explores the development and application of advanced ML and deep learning (DL) models for the interpretation of bioelectrical signals, such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). It is important to point out that non-invasive technologies such as brain-computer interfaces (BCIs) were used for the analysis of EEG signals.
The AI-driven models developed in this PhD thesis aim to enhance the accuracy and reliability of medical diagnostics, facilitating more precise and personalized clinical decisions. The integration of these models into clinical workflows has the potential to revolutionize patient care by providing healthcare professionals with powerful tools for diagnosis and treatment planning.
The practical outcomes of this research are profound, offering novel tools and frameworks that bridge the gap between AI innovation and clinical application. By incorporating explainable Artificial Intelligence (XAI) principles, the models developed in this thesis are designed to be transparent and interpretable, ensuring that healthcare professionals can trust and effectively use these advanced technologies in their daily practice.
In summary, this PhD thesis makes significant contributions to the intersection of AI and medicine, addressing key challenges in the interpretation of health and epidemiological data as well as the analysis of bioelectrical signals. The findings presented here lay a robust foundation for future advancements in personalized medicine and public health, ultimately aiming to improve patient outcomes and the overall efficacy of healthcare systems.
All contributions made in this thesis are detailed in the respective chapters, providing a comprehensive overview of the research conducted and its impact on the field of AI in healthcare
Using dual-network-analyser for communities detecting in dual networks
No full textBackground: Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. Results: We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. Conclusion: The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs
Two-Dimensional Kinetic Turbulence in the Solar Wind
No full textWe present the first 2D hybrid-Vlasov simulations of turbulence in the solar wind that describe the evolution of the energy spectra in a range of two decades of wavelengths around the ion inertial scale. Several previous magnetohydrodynamics and particle-in-cell simulations in the range of large (fluid) wavelengths showed a marked anisotropy of the energy spectra in the direction perpendicular to the mean magnetic field. Here we give evidence that the parallel direction can also be a privileged way for turbulence to develop towards short scales, where kinetic effects govern the plasma dynamics
Spatio-temporal resource mapping for intensive care units at regional level for COVID-19 emergency in Italy
No full textCOVID-19 is a worldwide emergency since it has rapidly spread from China to almost all the countries worldwide. Italy has been one of the most affected countries after China. North Italian regions, such as Lombardia and Veneto, had an abnormally large number of cases. COVID-19 patients management requires availability of sufficiently large number of Intensive Care Units (ICUs) beds. Resources shortening is a critical issue when the number of COVID-19 severe cases are higher than the available resources. This is also the case at a regional scale. We analysed Italian data at regional level with the aim to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, we retain that the here proposed model can be also used in other countries
The kinetic nature of turbulence at short scales in the solar wind
No full textThe analysis of the transition from the large-scale fluid regime to the short-scale kinetic range of wavelengths in the development of the turbulent cascade of energy is nowadays subject of fervent discussion in the space plasmas scientific community. We make use of Hybrid Vlasov-Maxwell simulations where the full kinetic dynamics of ions is taken into account, while electrons are treated as a fluid. We investigate the development of turbulence in the solar wind, in 1D-3V phase space configuration and in the frequency range across the ion cyclotron frequency. These simulations allow for the analysis of the role of kinetic effects in the short-scale region of the energy spectra in the direction parallel to the background magnetic field. Our numerical results show the presence of a significant electrostatic activity at small wavelengths, triggered by the resonant interaction of ions with longitudinal waves. Our model does not allow to take into account the evolution of the turbulent spectra in the plane perpendicular to the ambient field, due to limited dimensionality in phase space. On the other hand, this model permits to isolate and study the possibility of transferring the electromagnetic large-scale energy on the small-scale kinetic electrostatic component of the spectrum. Peculiar features observed in the spacecraft data in the solar wind are qualitatively reproduced within the hybrid-Vlasov model, such as the generation of perpendicular temperature anisotropy and accelerated longitudinal beams of ions in the distribution of particle velocities as well as the appearance of a marked peak of electrostatic activity in the short-scale termination of the turbulent spectra. (C) 2009 Elsevier Ltd. All rights reserved
Sharing mass spectrometry data in a grid-based distributed proteomics laboratory
No full textData produced by mass spectrometry (MS) have been using in proteomics experiments to identify proteins or patterns in clinical samples that may be responsible for human diseases. MS-based proteomics is becoming a powerful, widely used technique to identify different molecular targets in different pathological contexts. Moreover, MS samples contain a huge amount of data; retrieving such information requires accessing to large volumes of data, thus an efficient organization is necessary both to reduce access time and to allow efficient knowledge extraction. Bioinformatics laboratories have been using more than one mass spectrometer to improve efficiency, largely increasing the volume of data obtained by experiments. Moreover, experimental data is enriched by observations and descriptions added by specialists through metadata. Thus, information retrieval of spectra data (and metadata describing them) inside a laboratory and among different laboratories requires large and scalable storage solutions, and high performance computational platforms. We present a software system for managing, sharing, and querying MS data in a distributed laboratory, using a spectra data management system, called SpecDB, where information retrieval is performed by using computational grid facilities. Information retrieval can be conducted either locally, by considering portions of spectra data, or in a distributed scenario, exploiting metadata and annotations about spectra datasets stored on the grid
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