1,720,985 research outputs found
Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection
No full textThis thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection. It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management. The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data. Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards. The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS. Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring their combined potential to enhance CDSSs, particularly in the areas of COVID-19 and early sepsis detection. The thesis hopes to enhance understanding among medical stakeholders and acknowledges the need for continuous development in this sector.Denna avhandling presenterar en kritisk granskning av den positiva effekten av maskininlärning (ML) och Internet of Medical Things (IoMT) för att främja det kliniska beslutsstödsystemet (CDSS) kopplat till covid-19 och tidig upptäckt av sepsis. Avhandlingen betonar övergången mot patientcentrerade vårdsystem som kräver personlig och deltagande vård – en övergång som skulle kunna underlättas av dessa framväxande områden. Studien visar hur IoMT kan fungera som en robust plattform för dataaggregering, analys och överföring, vilket kan ge vårdgivare möjlighet att erbjuda mer effektiv vård. Covid-19-pandemin har särskilt betonat vikten av sådana patientcentrerade system för fjärrövervakning av patienter och sjukdomshantering. Integreringen av ML-drivna CDSS med IoMT ses som ett extremt viktigt steg i vårdsystemen som kan erbjuda stöd för beslutsfattande i realtid och förbättra patienternas hälsoutfall. Avhandlingen undersöker maskininlärningens förmåga att analysera komplexa medicinska dataset, identifiera mönster och korrelationer, samt göra anpassningar till föränderliga förhållanden, vilket därmed förbättrar dess prediktiva förmågor. Den fokuserar specifikt på utvecklingen av IoMT-baserade CDSS för covid-19 och tidig upptäckt av sepsis, med användning av avancerade ML-metoder och medicinska data. Nyckelfrågor som adresseras täcker bristen på dataannotering, dataspridning och dataheterogenitet, tillsammans med aspekter av säkerhet, integritet och tillgänglighet. Avhandlingen avser också att förbättra tolkbarheten av ML-prediktionsmodellbaserade CDSS. Etiska överväganden prioriteras för att säkerställa efterlevnad av de högsta standarderna. Avhandlingen demonstrerar potentialen och effektiviteten i att kombinera ML med IoMT för att förbättra CDSS genom att betona vikten av modelltolkbarhet, systemkompatibilitet och integrering av multimodala medicinska data för ett effektivt CDSS. Sammantaget bidrar denna avhandling till områdena ML och IoMT inom hälsovården, med deras kombinerade potential att förbättra CDSS, särskilt inom områdena covid-19 och tidig upptäckt av sepsis. Förhoppningen är att avhandlingen ska förbättra förståelsen bland medicinska intressenter och understryka behovet av kontinuerlig utveckling inom denna sektor
Behavioural determinants of landlord decisions on household wastewater management in urban Dhaka, Bangladesh
Get PDFAccess to safe sanitation remains a challenge in urban areas globally, particularly in low-and middle-income countries. In cities like Dhaka, Bangladesh, despite efforts to expand sewer connections, uptake among households remains low. This PhD synthesises findings from primary and secondary data to explore the drivers and motivators to sewer connection, identify enablers and barriers to sewer connection, and develop interventions to increase sewer connections in urban Dhaka.
I conducted a mixed-methods cross-sectional study across five Dhaka zones operated by the Dhaka Water and Sewerage Authority (DWASA). I surveyed 384 landlords, conducted 8 Key Informant Interviews with DWASA and other stakeholders, 10 In-Depth Interviews, and 2 Focus Group Discussions with landlords. Survey participants included households whose toilets were connected to the sewer, and those whose toilets were connected to stormsewer or drainage. I used the Risks, Attitudes, Norms, Abilities, and Self-regulation (RANAS) framework to examine socio-demographics, and psychological factors determining connection behaviour. I followed the thematic analysis technique for qualitative data analysis.
Evidence from the literature review indicated that behaviour change campaigns alone had limited effectiveness unless combined with financial incentives and community engagement strategies. Evidence from primary data indicated that individual-level barriers to sewer connection included the lack of knowledge about connection procedures and perceived low risks of disease transmission among households without sewer connections. Organisational-level barriers included complex administrative procedures, high installation costs, bureaucratic delays, inadequate support from DWASA, and outdated infrastructure, thus impeding the uptake of sewer connections.
To improve uptake, an integrated intervention model is proposed, which includes targeted educational campaigns to improve health knowledge, role model-based interventions to shift social norms, and technical support to facilitate the connection process. A holistic approach that combines educational efforts, behaviour change strategies, active community participation, and infrastructural improvements, can successfully overcome barriers to sewer connections.
Since the traditional behaviour change theories are more individual-focused, I also propose a broader, more integrated framework for sewer research and practice, a ‘behavioural informed system approach’ - that considers the complex interplay of infrastructural availability, financial investment, institutional trust, and social relations
Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection
No full textThis thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection. It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management. The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data. Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards. The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS. Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring their combined potential to enhance CDSSs, particularly in the areas of COVID-19 and early sepsis detection. The thesis hopes to enhance understanding among medical stakeholders and acknowledges the need for continuous development in this sector.Denna avhandling presenterar en kritisk granskning av den positiva effekten av maskininlärning (ML) och Internet of Medical Things (IoMT) för att främja det kliniska beslutsstödsystemet (CDSS) kopplat till covid-19 och tidig upptäckt av sepsis. Avhandlingen betonar övergången mot patientcentrerade vårdsystem som kräver personlig och deltagande vård – en övergång som skulle kunna underlättas av dessa framväxande områden. Studien visar hur IoMT kan fungera som en robust plattform för dataaggregering, analys och överföring, vilket kan ge vårdgivare möjlighet att erbjuda mer effektiv vård. Covid-19-pandemin har särskilt betonat vikten av sådana patientcentrerade system för fjärrövervakning av patienter och sjukdomshantering. Integreringen av ML-drivna CDSS med IoMT ses som ett extremt viktigt steg i vårdsystemen som kan erbjuda stöd för beslutsfattande i realtid och förbättra patienternas hälsoutfall. Avhandlingen undersöker maskininlärningens förmåga att analysera komplexa medicinska dataset, identifiera mönster och korrelationer, samt göra anpassningar till föränderliga förhållanden, vilket därmed förbättrar dess prediktiva förmågor. Den fokuserar specifikt på utvecklingen av IoMT-baserade CDSS för covid-19 och tidig upptäckt av sepsis, med användning av avancerade ML-metoder och medicinska data. Nyckelfrågor som adresseras täcker bristen på dataannotering, dataspridning och dataheterogenitet, tillsammans med aspekter av säkerhet, integritet och tillgänglighet. Avhandlingen avser också att förbättra tolkbarheten av ML-prediktionsmodellbaserade CDSS. Etiska överväganden prioriteras för att säkerställa efterlevnad av de högsta standarderna. Avhandlingen demonstrerar potentialen och effektiviteten i att kombinera ML med IoMT för att förbättra CDSS genom att betona vikten av modelltolkbarhet, systemkompatibilitet och integrering av multimodala medicinska data för ett effektivt CDSS. Sammantaget bidrar denna avhandling till områdena ML och IoMT inom hälsovården, med deras kombinerade potential att förbättra CDSS, särskilt inom områdena covid-19 och tidig upptäckt av sepsis. Förhoppningen är att avhandlingen ska förbättra förståelsen bland medicinska intressenter och understryka behovet av kontinuerlig utveckling inom denna sektor
Intelligent context-based healthcare metadata aggregator in internet of medical things platform
No full textThe internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application
No full textInternet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
Two decades of endemic dengue in Bangladesh (2000–2022): trends, seasonality, and impact of temperature and rainfall patterns on transmission dynamics
No full textThe objectives of this study were to compare dengue virus (DENV) cases, deaths, case-fatality ratio [CFR], and meteorological parameters between the first and the recent decades of this century (2000-2010 vs. 2011-2022) and to describe the trends, seasonality, and impact of change of temperature and rainfall patterns on transmission dynamics of dengue in Bangladesh. For the period 2000-2022, dengue cases and death data from Bangladesh's Ministry of Health and Family Welfare's website, and meteorological data from the Bangladesh Meteorological Department were analyzed. A Poisson regression model was performed to identify the impact of meteorological parameters on the monthly dengue cases. A forecast of dengue cases was performed using an autoregressive integrated moving average model. Over the past 23 yr, a total of 244,246 dengue cases were reported including 849 deaths (CFR = 0.35%). The mean annual number of dengue cases increased 8 times during the second decade, with 2,216 cases during 2000-2010 vs. 18,321 cases during 2011-2022. The mean annual number of deaths doubled (21 vs. 46), but the overall CFR has decreased by one-third (0.69% vs. 0.23%). Concurrently, the annual mean temperature increased by 0.49 °C, and rainfall decreased by 314 mm with altered precipitation seasonality. Monthly mean temperature (Incidence risk ratio [IRR]: 1.26), first-lagged rainfall (IRR: 1.08), and second-lagged rainfall (IRR: 1.17) were significantly associated with monthly dengue cases. The increased local temperature and changes in rainfall seasonality might have contributed to the increased dengue cases in Bangladesh
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