An HMM–ensemble approach to predict severity progression of ICU treatment for hospitalized Covid–19 patients

COVID–19–related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1,000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches

Data-Driven, AI-Based Clinical Practice:Experiences, Challenges, and Research Directions

Clinical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision that focuses on extending people's wellness state. This vision is increasingly data-driven, AI-based, and is underpinned by many forms of "Big Health Data" including periodic clinical assessments and electronic health records, but also using new forms of self-assessment, such as mobile-based questionnaires and personal wearable devices. Over the last few years, we have been conducting a fruitful research collaboration with the Infectious Disease Clinic of the University Hospital of Modena having the main aim of exploring specific opportunities offered by data-driven AI-based approaches to support diagnosis, hospital organization and clinical research. Drawing from this experience, in this paper we provide an overview of the main research challenges that need to be addressed to design and implement data-driven healthcare applications. We present concrete instantiations of these challenges in three real-world use cases and summarise the specific solutions we devised to address them and, finally, we propose a research agenda that outlines the future of research in this field.</p

Data-driven, AI-based clinical practice: experiences, challenges, and research directions

Clinical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision that focuses on extending people’s wellness state. This vision is increasingly data-driven, AI-based, and is underpinned by many forms of "Big Health Data" including periodic clinical assessments and electronic health records, but also using new forms of self-assessment, such as mobile-based questionnaires and personal wearable devices. Over the last few years, we have been conducting a fruitful research collaboration with the Infectious Disease Clinic of the University Hospital of Modena having the main aim of exploring specific opportunities offered by data-driven AI-based approaches to support diagnosis, hospital organization and clinical research. Drawing from this experience, in this paper we provide an overview of the main research challenges that need to be addressed to design and implement data-driven healthcare applications. We present concrete instantiations of these challenges in three real-world use cases and summarise the specific solutions we devised to address them and, finally, we propose a research agenda that outlines the future of research in this field

Real-world data mining meets clinical practice: Research challenges and perspective

As Big Data Analysis meets healthcare applications, domain-specific challenges and opportunities materialize in all aspects of data science. Advanced statistical methods and Artificial Intelligence (AI) on Electronic Health Records (EHRs) are used both for knowledge discovery purposes and clinical decision support. Such techniques enable the emerging Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm. Working with the Infectious Disease Clinic of the University Hospital of Modena, Italy, we have developed a range of Data-Driven (DD) approaches to solve critical clinical applications using statistics, Machine Learning (ML) and Big Data Analytics on real-world EHRs. Here, we describe our perspective on the challenges we encountered. Some are connected to medical data and their sparse, scarce, and unbalanced nature. Others are bound to the application environment, as medical AI tools can affect people's health and life. For each of these problems, we report some available techniques to tackle them, present examples drawn from our experience, and propose which approaches, in our opinion, could lead to successful real-world, end-to-end implementations. DESY report number: DESY-22-153

Application of 13C Quantitative NMR Spectroscopy to isotopic analyses for vanillin authentication source

6openInternationalBothThe carbon stable isotope ratio (δ13C) is a valuable chemical parameter in the investigation of the geographic origin, quality, and authenticity of foods. The aim of this study is the evaluation of the feasibility of 13C-NMR (Nuclear Magnetic Resonance) spectroscopy to determine the carbon stable isotope ratio, at natural abundance, of small organic molecules, such as vanillin, without the use of IRMS (Isotope Ratio Mass Spectrometry). The determination of vanillin origin is an active task of research, and differentiating between its natural and artificial forms is important to guarantee the quality of food products. To reach our goal, nine vanillin samples were analyzed using both 13C quantitative NMR spectroscopy (under optimized experimental conditions) and IRMS, and the obtained δ13C values were compared using statistical analysis (linear regression, Bland–Altman plot, and ANOVA (analysis of variance)). The results of our study show that 13C-NMR spectroscopy can be used as a valuable alternative methodology to determine the bulk carbon isotope ratio and to identify the origin of vanillin. This makes it attractive for the analysis in the same experiment of site-specific and total isotope effects for testing authenticity, quality, and typicality of food samples. Moreover, the improvement of NMR spectroscopy makes it possible to avoid the influence of additives on carbon stable isotope ratio analysis and to clearly identify fraud and falsification in commercial samplesopenPironti, C.; Ricciardi, M.; Motta, O.; Camin, F.; Bontempo, L.; Proto, A.Pironti, C.; Ricciardi, M.; Motta, O.; Camin, F.; Bontempo, L.; Proto, A

Um panorama da aqüicultura como alternativa sócio-econômica as comunidades tradicionais.

O rápido declínio dos recursos naturais disponíveis, indispensáveis à sobrevivência humana, produz efeitos negativos nas áreas social, ambiental e econômica. Declínio que leva ao deslocamento das populações tradicionais de seus territórios, em busca de fontes de renda alternativas capazes de garantir retorno econômico e sustento. A partir desse contexto, esse trabalho se propôs a descrever um cenário, através de vasta literatura disponível, de uma atividade que é recente no Brasil, mas com imenso potencial. Apresentando importantes aspectos que deverão ser abordados com maior atenção no futuro, se realmente a atividade for mais um instrumentos para melhoria das condições sócioeconômicas da população local, alívio da pressão sobre os estoques pesqueiros e incremento produtivo e renda

Histórico e desenvolvimento da maricultura no estado do Espírito Santo, Brasil.

A implantação da maricultura no Estado do Espírito Santo teve inicio de forma experimental no município de Piúma, no ano de 1987. Nos últimos 17 anos, a atividade foi implantada com fins comerciais em outros 5 (cinco) municípios através de projetos com objetivos similares e estratégias divergentes. Assim, o objetivo foi descrever a implantação da mitilicultura no Espírito Santo, levando-se em consideração os aspectos sócio-ambiental e econômico e características de cada “parque”. Os resultados apontaram para a importância do trabalho realizado junto à comunidade de forma a torná-la peça integrante dos processos de implantação e gestão do empreendimento, visando o desenvolvimento sustentável do mesmo

Digital for Heritage and Museums: Design-Driven Changes and Challenges

In the recent decade, cultural institutions have increasingly embraced digital technologies as key resources for accomplishing their mission and innovating their cultural activities. In the present work, we attempt to disentangle through a design-driven and multidisciplinary approach the challenges brought by digital transformation in the cultural heritage sector. A diversified research team has thus been involved to include scholars with different backgrounds around the common phenomenon of investigation of Digital (Cultural) Heritage, under the Design Think Thank project. The Introduction is followed by a Methodological section, which outlines the approach to select and review case studies from the exploratory literature for producing a state-of-the-art report and delineates the methodology to map the main user behaviours and needs in the digital experience of CH throughout the value chain. The research team identified three relevant and major themes for the investigation which are addressed in the Literature Review Section through the lenses of design research and practices; simultaneously, design knowledge emerges to have an agency in the transformation. The following section tries to triangulate the results from the literature review, and the mapping of users and stakeholders throughout the cultural institutions value chain, to track and highlight their role and interest in changing heritage panorama. The contribution of the present work wishes to consolidate the results gathered in the first phases of the TT, providing the design community of academics and practitioners with a theoretical contribution about digital changes and challenges of heritage and museums based on a design perspective

The effect of different volumes and temperatures of saline on the bladder pressure measurement in critically ill patients

INTRODUCTION: Intra-abdominal hypertension is common in critically ill patients and is associated with increased severity of organ failure and mortality. The techniques most commonly used to estimate intra-abdominal pressure are measurements of bladder and gastric pressures. The bladder technique requires that the bladder be infused with a certain amount of saline, to ensure that there is a conductive fluid column between the bladder and the transducer. The aim of this study was to evaluate the effect of different volumes and temperatures of infused saline on bladder pressure measurements in comparison with gastric pressure. METHODS: Thirteen mechanically ventilated critically ill patients (11 male; body mass index 25.5 +/- 4.6 kg/m2; arterial oxygen tension/fractional inspired oxygen ratio 225 +/- 48 mmHg) were enrolled. Bladder pressure was measured using volumes of saline from 50 to 200 ml at body temperature (35 to 37 degrees C) and room temperature (18 to 20 degrees C). RESULTS: Bladder pressure was no different between 50 ml and 100 ml saline (9.5 +/- 3.7 mmHg and 13.7 +/- 5.6 mmHg), but it significantly increased with 150 and 200 ml (21.1 +/- 10.4 mmHg and 27.1 +/- 15.5 mmHg). Infusion of saline at room temperature caused a significantly greater bladder pressure compared with saline at body temperature. The lowest difference between bladder and gastric pressure was obtained with a volume of 50 ml. CONCLUSION: The bladder acts as a passive structure, transmitting intra-abdominal pressure only with saline volumes between 50 ml and 100 ml. Infusion of a saline at room temperature caused a higher bladder pressure, probably because of contraction of the detrusor bladder muscl