Hasil untuk "Public aspects of medicine"

Emily J. Bian, Priyanka Menon, Kathleen A. McManus et al.

Stroke prevalence is highest in adults ≥65 years, the majority of whom are Medicare beneficiaries. Feefor- Service Medicare (FFS) incentivizes utilization by paying for each service. Medicare Advantage (MA) uses capitated payments to reduce overutilization. It is not clear if stroke patients with FFS or MA receive different stroke preventive care and whether those differences are associated with differences in postacute care utilization, cost and clinical outcomes.We performed an empirical narrative review of published peer-reviewed studies in the PubMed, EMBASE andWeb of Science databases comparing stroke preventive care between FFS and MA using the American Heart Association’s Life’s Essential 8 and American Heart Association/American Stroke Association national guidelines.We added atrial fibrillation (AF), post-acute care utilization and outcomes, including mortality. 7/1356 studies met inclusion criteria. Studies were heterogenous in their design and settings. There was limited availability of clinical data. Within those limitations, published studies suggest that MA appears to allow for guideline-directed stroke preventive care for hyperlipidemia, smoking cessation and AF in specific study populations. Post-acute care utilization was generally lower in MA. Functional outcomes improvements were similar but occurred in fewer days in MA, though the absence of acute stroke treatment data is notable. Mortality data were mixed. Given the importance of stroke in Medicare and the growth in MA enrollment, comparing the effectiveness of MA and FFS warrants further study among appropriately matched MA and FFS beneficiaries with stroke.

Ellen Johnson-Sirleaf

Yuxuan Zhao, Kaisheng Zhu, Yefei Zhang et al.

The costly provision of public goods serves as a model problem for the evolution of cooperative behavior, presenting a social dilemma between the collective benefits of shared resources and the individual incentive to free-ride in resource production. The spatial structure of populations can also impact cooperation over public goods, as diffusion of public goods and intentional motion of individuals towards regions with greater resources can interact with population and public goods dynamics to produce heterogeneous patterns in the spatial distribution of strategies and resources. In this paper, we build off a model introduced by Young and Belmonte for the reaction dynamics of interacting individuals and explicit public good, deriving a system of PDEs that describes the spatial profiles of strategies and the public good in the presence of both diffusive motion of individuals and resources and chemotaxis-like directed motion of individuals in response to gradients in the concentration of public goods. Through linear stability analysis, we show that spatial patterns in strategic and public goods profiles can emerge due to either Turing instability with high defector diffusivity or a directed-motion instability through strong sensitivity of cooperators towards increasing resource concentration. We further explore the emergent spatial patterns with a mix of weakly nonlinear stability analysis and numerical simulation, showing that diffusion-driven instability appears to increase cooperation and public goods across the spatial domain, while directed motion of cooperators towards regions with great public goods provision tends to decrease cooperation and environmental quality across the environment.

Jonathan Engle, Bryce Morsky

We model an electorate voting on the funding of a public good in a two-party system in an evolutionary game theory framework. Voters adopt one of four strategies: Consensus-makers, Gridlockers, Party 1 Zealots, and Party 2 Zealots, which they may change via imitation. The public good benefits both individuals locally and those in neighbouring regions due to spillover effects. A system of differential equations governs the spatial movement of individuals and shifts in their voting strategies. Local social interactions drive strategy evolution, while migration occurs toward areas of higher utility, which is a function of both social and economic factors. Our results reveal bistability and significant spatial variations. Locally, populations converge to a politically gridlocked state or a mix of consensus-makers and zealots, determining public good provisioning. We find that public good spillovers generate a free-rider effect and poorly funded regions become spatially tied to, and dependent upon, well-funded ones.

Gege Zhang, Sijie Dong, Li Wang

Abstract Background The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors. Methods Based on data from the China Health and Retirement Longitudinal Study (CHARLS) from 2018 to 2020, 4,477 patients aged 45 and above were included. LASSO regression was used to screen for risk factors, and three machine learning algorithms—logistic regression (LR), random forest (RF), and XGBoost—were employed to build predictive models. The performance of the models was evaluated using ROC curves, calibration curves, and decision curves. Results The study found several risk factors significantly associated with depression, including disability status, pain, retirement status, number of chronic diseases, education level, age, gender, place of residence, life satisfaction, optimism about the future, and self-rated health status. The incidence of depression was significantly higher among women (56%), rural residents (64%), individuals with disabilities, non-retirees (85%), and those with chronic illnesses (73%). The LR model demonstrated the best predictive performance, with an AUC of 0.69. Key predictive factors included self-rated health, residence, education level, gender, pain, life satisfaction, age, and hope for the future. Conclusion This study developed a depression risk prediction model based on logistic regression, providing important references for psychological health interventions in middle-aged and elderly patients with CMD. Identifying and intervening in high-risk populations is crucial for improving patients' quality of life.

Akanksha Arya, Owen Renault, William Eissler et al.

Background: There is a high prevalence of catheter associated urinary tract infections (CAUTIs) on a hospital cardiology unit, with a rate of 2.48 CAUTIs per 1,000 catheter days over the past two years compared to the national average of 0.96 CAUTIs for similar units. CAUTIs lead to increased lengths of stay, mortality, and hospital expenditures. Per NHSN, the presence of an indwelling urinary catheter (IUC) increases the risk for developing a CAUTI by 3-7% each day an IUC is in place. Method: A process improvement approach was utilized to study the problem of increased CAUTIs and implement a PDSA intervention.

Youngseung Jeon, Christopher Hwang, Ziwen Li et al.

While drug discovery is vital for human health, the process remains inefficient. Medicinal chemists must navigate a vast protein space to identify target proteins that meet three criteria: physical and functional interactions, therapeutic impact, and docking potential. Prior approaches have provided fragmented support for each criterion, limiting the generation of promising hypotheses for wet-lab experiments. We present HAPPIER, an AI-powered tool that supports hypothesis generation with integrated multi-criteria support for target identification. HAPPIER enables medicinal chemists to 1) efficiently explore and verify proteins in a single integrated graph component showing multi-criteria satisfaction and 2) validate AI suggestions with domain knowledge. These capabilities facilitate iterative cycles of divergent and convergent thinking, essential for hypothesis generation. We evaluated HAPPIER with ten medicinal chemists, finding that it increased the number of high-confidence hypotheses and support for the iterative cycle, and further demonstrated the relationship between engaging in such cycles and confidence in outputs.

Michael S. Yao, Osbert Bastani, Alma Andersson et al.

The goal of personalized medicine is to discover a treatment regimen that optimizes a patient's clinical outcome based on their personal genetic and environmental factors. However, candidate treatments cannot be arbitrarily administered to the patient to assess their efficacy; we often instead have access to an in silico surrogate model that approximates the true fitness of a proposed treatment. Unfortunately, such surrogate models have been shown to fail to generalize to previously unseen patient-treatment combinations. We hypothesize that domain-specific prior knowledge - such as medical textbooks and biomedical knowledge graphs - can provide a meaningful alternative signal of the fitness of proposed treatments. To this end, we introduce LLM-based Entropy-guided Optimization with kNowledgeable priors (LEON), a mathematically principled approach to leverage large language models (LLMs) as black-box optimizers without any task-specific fine-tuning, taking advantage of their ability to contextualize unstructured domain knowledge to propose personalized treatment plans in natural language. In practice, we implement LEON via 'optimization by prompting,' which uses LLMs as stochastic engines for proposing treatment designs. Experiments on real-world optimization tasks show LEON outperforms both traditional and LLM-based methods in proposing individualized treatments for patients.

Daniel Mas Montserrat, Ray Verma, Míriam Barrabés et al.

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variability in per-chromosome RAM demands, resulting in poor resource utilization and long runtimes. In this work, we propose multiple mechanisms for adaptive, RAM-efficient parallelization of chromosome-level bioinformatics workflows. First, we develop a symbolic regression model that estimates per-chromosome memory consumption for a given task and introduces an interpolating bias to conservatively minimize over-allocation. Second, we present a dynamic scheduler that adaptively predicts RAM usage with a polynomial regression model, treating task packing as a Knapsack problem to optimally batch jobs based on predicted memory requirements. Additionally, we present a static scheduler that optimizes chromosome processing order to minimize peak memory while preserving throughput. Our proposed methods, evaluated on simulations and real-world genomic pipelines, provide new mechanisms to reduce memory overruns and balance load across threads. We thereby achieve faster end-to-end execution, showcasing the potential to optimize large-scale genomic workflows.

Yvonne Anne Michel, Eline Aas, Liv Ariane Augestad et al.

Abstract Background Existing knowledge on healthcare use and costs in the last months of life is often limited to one patient group (i.e., cancer patients) and one level of healthcare (i.e., secondary care). Consequently, decision-makers lack knowledge in order to make informed decisions about the allocation of healthcare resources for all patients. Our aim is to elaborate the understanding of resource use and costs in the last six months of life by describing healthcare use and costs for all causes of death and by all levels of formal care. Method Using five national registers, we gained access to patient-level data for all individuals who died in Norway between 2009 and 2013. We described healthcare use and costs for all levels of formal care—namely primary, secondary, and home- and community-based care —in the last six months of life, both in total and differentiated across three time periods (6-4 months, 3-2 months, and 1-month before death). Our analysis covers all causes of death categorized in ten ICD-10 categories. Results During their last six months of life, individuals used an average of healthcare resources equivalent to €46,000, ranging from €32,000 (Injuries) to €64,000 (Diseases of the nervous system and sense organs). In terms of care level, 63% of healthcare resources were used in home- and community-based care (i.e., in-home nursing, practical assistance, or nursing home care), 35% in secondary care (mostly hospital care), and 2% in primary care (i.e., general practitioners). The amount and level of care varied by cause of death and by time to death. The proportion of home- and community-based care which individuals received during their last six months of life varied from 38% for cancer patients to 92% for individuals dying with mental diseases. The shorter the time to death, the more resources were needed: nearly 40% of all end-of-life healthcare costs were expended in the last month of life across all causes of death. The composition of care also differed depending on age. Individuals aged 80 years and older used more home- and community-based care (77%) than individuals dying at younger ages (40%) and less secondary care (old: 21% versus young: 57%). Conclusions Our analysis provides valuable evidence on how much healthcare individuals receive in their last six months of life and the associated costs, broken down by level of care and cause of death. Healthcare use and costs varied considerably by cause of death, but were generally higher the closer a person was to death. Our findings enable decision-makers to make more informed resource-allocation decisions and healthcare planners to better anticipate future healthcare needs.

Molud Esmaili, Ken Christensen

Public permissionless blockchains facilitate peer-to-peer digital transactions, yet face performance challenges specifically minimizing transaction confirmation time to decrease energy and time consumption per transaction. Performance evaluation and prediction are crucial in achieving this objective, with performance modeling as a key solution despite the complexities involved in assessing these blockchains. This survey examines prior research concerning the performance modeling blockchain systems, specifically focusing on public permissionless blockchains. Initially, it provides foundational knowledge about these blockchains and the crucial performance parameters for their assessment. Additionally, the study delves into research on the performance modeling of public permissionless blockchains, predominantly considering these systems as bulk service queues. It also examines prior studies on workload and traffic modeling, characterization, and analysis within these blockchain networks. By analyzing existing research, our survey aims to provide insights and recommendations for researchers keen on enhancing the performance of public permissionless blockchains or devising novel mechanisms in this domain.

Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth

The expansion of urban centers necessitates enhanced efficiency and sustainability in their transportation infrastructure and mobility systems. The big data obtainable from various transportation modes potentially offers critical insights for urban planning. This study presents analysis of detailed historical public transit data, enriched with relevant temporal and geospatial metadata, as a precursor to injecting dynamism into digital twins of mobility systems via ML/DL-based predictive modeling. A data preprocessing framework was implemented to refine the raw data for effective historical analysis and predictive modeling. This paper examines public transit data for patterns and trends -- incorporating factors such as time, geospatial elements, external influences, and operational aspects. From a technical standpoint, this research helps to assess the quality of the available transit data and identify important information for use in digital twins. Such digital twins foster educated decisions for efficient, sustainable urban mobility systems by anticipating infrastructure demand, identifying service gaps, and understanding mobility dynamics.

Ziba Moravej, Gholamali Haghighat, Aboubakr Jafarnezhad et al.

Background: Childhood hypertension can have serious consequences for children, especially during their adulthood. This study aimed to determine the prevalence of hypertension and its related factors in children aged 7 to 12 years in Larestan (Iran). Methods: In this cross-sectional study, 1110 students from 7 to 12 years old in Larestan were enrolled in the study using cluster sampling. In order to collect educational data, 10 girls' primary schools and 10 boys' primary schools were randomly selected from all the girls' and boys' primary schools. Then, considering that each school has different number of students, 55 students of each school were randomly selected. Students' blood pressure was measured as standard. Their personal information was also recorded in a checklist. A checklist containing demographic information, factors and variables that affect the prevalence of hypertension in children aged 7 to 12 years was used. To collect information, a checklist was used that included demographic information such as age, gender, and place of residence. Also, in order to identify factors and variables effective in the prevalence of high blood pressure (HBP), tools such as sphygmomanometer and other risk factors including obesity, type of diet, physical activities, and body mass index (BMI) were used. Results: The mean age of girls was 9.11 ± 1.53 and boys were 9.19 ± 1.52, which did not differ significantly from the statistical point of view. The prevalence of pre hypertension was 6.03% (95% CI: 4.71-7.60) and the prevalence of hypertension was 4.14% (95% CI: 3.05-5.49). Systolic and diastolic blood pressure has direct relation with height and weight of children. Diastolic blood pressure also had a higher prevalence in girls (P < 0.001). Conclusion: Overweight and obesity as a moderate aggressive factor were significantly associated with blood pressure. Also, the prevalence of HBP in children was significant, and it is necessary to pay attention to it in childhood. Moreover, hypertension cases should be identified and treatment should start faster for the affected person to prevent the adverse consequences in the future.

Weihong Qi, Jinsheng Pan, Hanjia Lyu et al.

As AI systems become increasingly prevalent in various aspects of daily life, gaining a comprehensive understanding of public perception towards these AI systems has become increasingly essential for several reasons such as ethical considerations, user experience, fear, disinformation, regulation, collaboration, and co-creation. In this study, we investigate how mass social media users perceive the recent rise of AI frameworks such as ChatGPT. We collect a total of 33,912 comments in 388 unique subreddits spanning from November 30, 2022 to June 8, 2023 using a list of AI-related keywords. We employ BERTopic to uncover the major themes regarding AI on Reddit. Additionally, we seek to gain deeper insights into public opinion by examining the distribution of topics across different subreddits. We observe that technology-related subreddits predominantly focus on the technical aspects of AI models. On the other hand, non-tech subreddits show greater interest in social issues such as concerns about job replacement or furlough. We leverage zero-shot prompting to analyze the sentiment and perception of AI among individual users. Through a comprehensive sentiment and emotion analysis, we discover that tech-centric communities exhibit greater polarization compared to non-tech communities when discussing AI topics. This research contributes to our broader understanding of public opinion surrounding artificial intelligence.

Doratossadat Dastgheib, Hadi Farahani

In this paper, we propose a doxastic extension $BL^+$ of Lukasiewicz logic which is sound and complete relative to the introduced corresponding semantics. Also, we equip our doxastic Lukasiewicz logic $BL^+$ with public announcement and propose the logic $DL$. As an application, we model a fuzzy version of muddy children puzzle with public announcement using $DL$. Finally, we define a translation between $DL$ and $BL^+$, and prove the soundness and completeness theorems for D L

Yalin Zhai, Geng Hong, Mengnan Jiang et al.

With the outbreak of coronavirus disease 2019 (COVID-19), it is essential to share pathogens and their data information safely, transparently, and timely. At the same time, it is also worth exploring how to share the benefits of using the provided pathogenic microorganisms fairly and equitably. There are some mechanisms for the management and sharing of pathogenic microbial resources in the world, such as the World Health Organization (WHO), the United States, the Europe, and China. This paper studies these mechanisms and puts forward “PICC” principles, including public welfare principle, interests principle, classified principle, and category principle, to strengthen cooperation, improve efficiency, and maintain biosafety.

Francileudo Santos de Abreu, Geraldo Bezerra da Silva Junior

Objetivo: Investigar as características sociodemográficas, ocupacionais e de morbidade dos servidores que tiveram licenças por transtorno mental e comportamental (TMC), bem como as associações dessas características com o afastamento precoce. Métodos: Estudo longitudinal (coorte retrospectiva), realizado no Instituto Federal de Educação do Ceará (IFCE), com vistas ao delineamento do perfil epidemiológico dos servidores afastados por TMC (n=250), no período de 2010 a 2018, e as associações das características epidemiológicas (sexo, grupo do cargo, local de trabalho, tempo na instituição, estado civil, faixa etária e remuneração) com o afastamento precoce, através das curvas de sobrevivência de Kaplan-Meier. Resultados: Ocorreram 684 afastamentos por TMC em 250 servidores (incidência de 4,9%), resultando em 22.409 dias perdidos de trabalho (DAW) e efeitos financeiros de aproximadamente R$ 6.845.220. Houve um aumento na quantidade de afastamentos, de DAW e da taxa de incidência de servidores afastados ao longo do período do estudo. O grupo dos transtornos do humor (F30-F39) da CID-10 apresentou-se como a principal causa de afastamento por TMC (n=367; 53,6%) e DAW (13.057). A análise de sobrevida mostrou afastamento precoce nos servidores do interior, solteiros, com faixa etária de 18 a 39 anos, tempo de serviço na instituição de até 9 anos e classe econômica C. Conclusão: Houve crescimento no IFCE, entre 2010 e 2018, dos afastamentos, dos DAW e da incidência de servidores afastados por TMC, com predominância dos transtornos do humor, os quais geraram efeitos financeiros elevados para a instituição. Evidenciou-se associação positiva entre algumas características sociodemográficas e o afastamento precoce por TMC.

Zewei Li, James C. Spall

Many public health threats exist, motivating the need to find optimal intervention strategies. Given the stochastic nature of the threats (e.g., the spread of pandemic influenza, the occurrence of drug overdoses, and the prevalence of alcohol-related threats), deterministic optimization approaches may be inappropriate. In this paper, we implement a stochastic optimization method to address aspects of the 2009 H1N1 and the COVID-19 pandemics, with the spread of disease modeled by the open source Monte Carlo simulations, FluTE and Covasim, respectively. Without testing every possible option, the objective of the optimization is to determine the best combination of intervention strategies so as to result in minimal economic loss to society. To reach our objective, this application-oriented paper uses the discrete simultaneous perturbation stochastic approximation method (DSPSA), a recursive simulation-based optimization algorithm, to update the input parameters in the disease simulation software so that the output iteratively approaches minimal economic loss. Assuming that the simulation models for the spread of disease (FluTE for H1N1 and Covasim for COVID-19 in our case) are accurate representations for the population being studied, the simulation-based strategy we present provides decision makers a powerful tool to mitigate potential human and economic losses from any epidemic. The basic approach is also applicable in other public health problems, such as opioid abuse and drunk driving.

Jonas Sylneon

In this article, we develop a deterministic compartmental model including the spatio-temporal aspect and the wearing of masks by the general public for the spread of the Covid-19 epidemic. This model is based on the SIR model of Kermack and McKendrick and includes mobility terms that model the movement of individuals wearing masks between Guadeloupe andMartinique. The objective of the model is to analyze the effects of mobility and the wearing of masks by the general public on the spread of the Covid-19 epidemic in these two regions.Numerical simulations of this model show the importance of wearing masks by the general public, even if they are not very inefficient, and indicate that the model is capable of qualitatively simulating the propagation trends of the Covid-19 epidemic. This work is part ofmy doctoral thesis, the subject of which is entitled « stochastic modeling of Covid-19 in an island environment taking into account the spatio-temporal aspect ».

Andrea Campagner, Lorenzo Famiglini, Anna Carobene et al.

In medical settings, Individual Variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given instance or the measurement process. While taking into account IV has been deemed critical for proper analysis of medical data, this source of uncertainty and its impact on robustness have so far been neglected in Machine Learning (ML). To fill this gap, we look at how IV affects ML performance and generalization and how its impact can be mitigated. Specifically, we provide a methodological contribution to formalize the problem of IV in the statistical learning framework and, through an experiment based on one of the largest real-world laboratory medicine datasets for the problem of COVID-19 diagnosis, we show that: 1) common state-of-the-art ML models are severely impacted by the presence of IV in data; and 2) advanced learning strategies, based on data augmentation and data imprecisiation, and proper study designs can be effective at improving robustness to IV. Our findings demonstrate the critical relevance of correctly accounting for IV to enable safe deployment of ML in clinical settings.