Hasil untuk "Public aspects of medicine"

Zhanyu Liu, Yang Yu

Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate attention within the realm of quantitative research in public administration and the broader social sciences. The advent of interpretable machine learning presents a significant opportunity to integrate prediction into quantitative research conducted in public administration. This article delves into the fundamental principles of interpretable machine learning while also examining its current applications in social science research. Building upon this foundation, the article further expounds upon the implementation process of interpretable machine learning, encompassing key aspects such as dataset construction, model training, model evaluation, and model interpretation. Lastly, the article explores the disciplinary value of interpretable machine learning within the field of public administration, highlighting its potential to enhance the generalization of inference, facilitate the selection of optimal explanations for phenomena, stimulate the construction of theoretical hypotheses, and provide a platform for the translation of knowledge. As a complement to traditional causal inference methods, interpretable machine learning ushers in a new era of credibility in quantitative research within the realm of public administration.

Aahsan Iqbal, Sohail Khalid, Mujeeb ur Rehman

Hematology analyzers are essential diagnostic and monitoring tools for detecting blood diseases. Although contemporary analyzers produce only basic insights, they are often not as detailed as required under the personalized medicine paradigm. Next-Generation Hematology Analyzers (NGHAs) are revolutionary newcomers in the field, with significant advantages over regular hematology analyzers. They provide deeper insights into cellular morphology, function, and genetic profiles. This detailed information opens up possibilities for tailor-made diagnostic and therapeutic approaches in precision medicine. This review presents some revolutionary technologies that have changed hematology analyzers and provides an overview of their limitations, basic functions, and influence on clinical practice. It focuses on the integration of state-of-the-art technologies, such as microfluidics, advanced optics, artificial intelligence, flow cytometry, and digital imaging, empowering NGHAs to improve diagnostic accuracy, rapidly detect diseases, and support flexible, targeted therapy. Hints regarding point-of-care hematology testing are also provided to discuss its implications for transforming healthcare patterns. This review highlights the data management, standardization, regulatory, and ethical challenges associated with these technologies. A review tracking the current state-of-the-art and trends for the future is provided to show how these advancements may reconfigure hematology analyzer design and act as a stepping stone for future therapeutic reforms.

Junying Chen, Zhenyang Cai, Zhiheng Liu et al.

Despite the success of large language models (LLMs) in various domains, their potential in Traditional Chinese Medicine (TCM) remains largely underexplored due to two critical barriers: (1) the scarcity of high-quality TCM data and (2) the inherently multimodal nature of TCM diagnostics, which involve looking, listening, smelling, and pulse-taking. These sensory-rich modalities are beyond the scope of conventional LLMs. To address these challenges, we present ShizhenGPT, the first multimodal LLM tailored for TCM. To overcome data scarcity, we curate the largest TCM dataset to date, comprising 100GB+ of text and 200GB+ of multimodal data, including 1.2M images, 200 hours of audio, and physiological signals. ShizhenGPT is pretrained and instruction-tuned to achieve deep TCM knowledge and multimodal reasoning. For evaluation, we collect recent national TCM qualification exams and build a visual benchmark for Medicinal Recognition and Visual Diagnosis. Experiments demonstrate that ShizhenGPT outperforms comparable-scale LLMs and competes with larger proprietary models. Moreover, it leads in TCM visual understanding among existing multimodal LLMs and demonstrates unified perception across modalities like sound, pulse, smell, and vision, paving the way toward holistic multimodal perception and diagnosis in TCM. Datasets, models, and code are publicly available. We hope this work will inspire further exploration in this field.

Mahmoud Alwakeel, Aditya Nagori, An-Kwok Ian Wong et al.

Large Language Models have been tested on medical student-level questions, but their performance in specialized fields like Critical Care Medicine (CCM) is less explored. This study evaluated Meta-Llama 3.1 models (8B and 70B parameters) on 871 CCM questions. Llama3.1:70B outperformed 8B by 30%, with 60% average accuracy. Performance varied across domains, highest in Research (68.4%) and lowest in Renal (47.9%), highlighting the need for broader future work to improve models across various subspecialty domains.

Medha Mathur, Dewesh Kumar

Yang Zhu, Wen-Ming Liang, Kai Jiang et al.

Abstract Background Questionnaires that assess psychological functioning are 21 limited by their subjective nature, while HRV can serve as a more objective 22 (but also complex) index of such functioning. This study aims to validate sex 23 differences in college students' mental well-being using psychological scales 24 and HRV, and to investigate the correlation between psychological scales 25 and HRV for each sex. Method 240 college students (120 males and 120 females, aged 18-22 27 years) were recruited via cluster sampling from 1st Sept. to 1st Nov. 2023 at 28 Zhejiang University in China. Mental well-being was assessed using the 29 Warwick-Edinburgh Mental Well-being Scale (WEMWBS) and the 21-item 30 version of the Depression, Anxiety, and Stress Scale (DASS-21), while HRV 31 was measured at rest using a Polar H7 heart rate monitor. Results Comparative analyses showed that female students had higher 33 anxiety scores (DASS-21) (p = 0.033, Partial η² = 0.019) and lower mental 34 well-being scores (WEMWBS) (p = 0.047, Partial η² = 0.016) compared to 35 male students. Additionally, female students exhibited lower HRV across 36 multiple indices, including SDNN (p < 0.001, Partial η² = 0.158), RMSSD (p 37 < 0.001, Partial η² = 0.064), pNN50 (p < 0.001, Partial η² = 0.045), and 38 absolute high-frequency (HF) power (p = 0.003, Partial η² = 0.038). 39 Correlational analyses further revealed that only female students' anxiety 40 scores were negatively associated with RMSSD (r = -0.245, p = 0.008), 41 absolute HF power (r = -0.261, p = 0.005), and normalized HF power (r = - 42 0.262, p = 0.005). Conclusions Female university students exhibited poorer mental well-being 44 than male students, as indicated by both subjective and objective measures, with anxiety being particularly prominent. Combining psychological scales 46 with measures of HRV (RMSSD and HF power) may improve anxiety 47 assessment in female university students.

Amaka Perpetual Muoneke, Victor Godwin Essien, Tolulope Joseph Ogunniyi et al.

Abstract Nigeria has recorded recurrent Mpox outbreaks since the 1970s, with the most recent resurgence underscoring weaknesses in the country’s diagnostic and laboratory infrastructure. Despite improvements in molecular diagnostics, such as genome sequencing and polymerase chain reaction (PCR), limited access to testing facilities, shortages of trained personnel, and inadequate biosafety measures remains a significant challenge. Although collaborations with international partners during recent outbreaks of infectious diseases have enhanced diagnostic capacity. The COVID-19 pandemic led to investments that expanded diagnostic infrastructure and decentralized sample collection, yet logistical barriers persist, particularly in rural areas where inadequate transportation networks and power supply disruptions delay sample processing. Additionally, workforce shortages and high emigration rates among laboratory personnel further weakened the system’s efficiency. Therefore, this review evaluates Nigeria’s preparedness for Mpox surveillance and response by analyzing past outbreak management efforts, including Ebola and Lassa fever. It identifies key strengths and gaps in the laboratory system, highlights barriers to effective diagnostics, and explores opportunities for improvement in terms of upgrading biosafety infrastructure, capacity building of healthcare workers, fostering public-private partnerships to develop local diagnostic tools, and increasing government funding for laboratory services. Strengthening these areas is essential for improving Nigeria’s capacity to detect and contain Mpox outbreaks and enhancing overall public health preparedness.

In-Gyu Lee, Jun-Young Oh, Hee-Jung Yu et al.

Recently, with increasing interest in pet healthcare, the demand for computer-aided diagnosis (CAD) systems in veterinary medicine has increased. The development of veterinary CAD has stagnated due to a lack of sufficient radiology data. To overcome the challenge, we propose a generative active learning framework based on a variational autoencoder. This approach aims to alleviate the scarcity of reliable data for CAD systems in veterinary medicine. This study utilizes datasets comprising cardiomegaly radiograph data. After removing annotations and standardizing images, we employed a framework for data augmentation, which consists of a data generation phase and a query phase for filtering the generated data. The experimental results revealed that as the data generated through this framework was added to the training data of the generative model, the frechet inception distance consistently decreased from 84.14 to 50.75 on the radiograph. Subsequently, when the generated data were incorporated into the training of the classification model, the false positive of the confusion matrix also improved from 0.16 to 0.66 on the radiograph. The proposed framework has the potential to address the challenges of data scarcity in medical CAD, contributing to its advancement.

Manlio De Domenico, Luca Allegri, Guido Caldarelli et al.

The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.

María Isabel Roldós, Jaye Jones, Jocelyn Rajaballey

Introduction: Community-based participatory research (CBPR) is one of the most effective strategies for conceptualizing, developing, and executing programs or interventions that address health disparities in community settings. The City University of New York (CUNY)'s Institute for Health Equity (CIHE) focuses on the social determinants that affect the physical and mental health of New York City's poor and underserved. Methods: This study utilized a modified Strengths, Weaknesses, Opportunities, and Threats (SWOT) tool as a strength-based assessment (SBA) to evaluate community-based organization (CBO)'s Areas for Growth (SWOT-SBA). This approach was used to identify CBOs' strengths, prospects, and priorities to address the Bronx's health disparities. Furthermore, this study collected descriptive information on CBO's catchment areas, services provided, and population served to create interactive and static maps and contingency tables using the Arch-GIS software. Results: This study was the first step to building CIHE Healthy-Bronx Research Collaborative to address the Bronx's health disparities. The results indicate that Hunts Point and Longwood Community Districts are the most served by CBOs. The SWOT-SBA suggests that CBOs' engagement through “appreciative inquiry” to conduct a CBPR has the most promise for a successful partnership between CBOs, research partners, and local stakeholders. Conclusion: This analysis suggests that CBOs center their resources to function as a leader in the Bronx and have identified the need to expand services during the pandemic. Findings from this study suggest that CBOs want to collaborate in CBPR initiatives.

Vranić Lana, Biloglav Zrinka, Medaković Petar et al.

The aim of this study was to estimate the effects of a pulmonary rehabilitation programme (PR) on the functional capacity and respiratory muscle strength of patients with post-COVID syndrome.

Vahid Reza Nafisi, Roshanak Ghods

Persian Medicine (PM) uses wrist temperature/humidity and pulse to determine a person's health status and temperament. However, the diagnosis may depend on the physician's interpretation, hindering the combination of PM with modern medical methods. This study proposes a system for measuring pulse signals and temperament detection based on PM. The system uses recorded thermal distribution, a temperament questionnaire, and a customized pulse measurement device. The collected data can be sent to a physician via a telecare system for interpretation and prescription of medications. The system was clinically implemented for patient care, assessed the temperaments of 34 participants, and recorded thermal images of the wrist, back of the hand, and entire face. The study suggests that a customized device for measuring pulse waves and other criteria based on PM can be incorporated into a telemedicine system, reducing the dependency on PM specialists for diagnosis.

Alireza Mohammadinodooshan, Niklas Carlsson

The rapid growth of social media as a news platform has raised significant concerns about the influence and societal impact of biased and unreliable news on these platforms. While much research has explored user engagement with news on platforms like Facebook, most studies have focused on publicly shared posts. This focus leaves an important question unanswered: how representative is the public sphere of Facebook's entire ecosystem? Specifically, how much of the interactions occur in less-public spaces, and do public engagement patterns for different news classes (e.g., reliable vs. unreliable) generalize to the broader Facebook ecosystem? This paper presents the first comprehensive comparison of interaction patterns between Facebook's more public sphere (referred to as public in paper) and the less public sphere (referred to as private). For the analysis, we first collect two complementary datasets: (1) aggregated interaction data for all Facebook posts (public + private) for 19,050 manually labeled news articles (225.3M user interactions), and (2) a subset containing only interactions with public posts (70.4M interactions). Then, through discussions and iterative feedback from the CrowdTangle team, we develop a robust method for fair comparison between these datasets. Our analysis reveals that only 31% of news interactions occur in the public sphere, with significant variations across news classes. Engagement patterns in less-public spaces often differ, with users, for example, engaging more deeply in private contexts. These findings highlight the need to examine both public and less-public engagement to fully understand news dissemination on Facebook. The observed differences hold important implications on content moderation, platform governance, and policymaking, contributing to healthier online discourse.

Lenore Manderson, Sara Jewett

Abstract Common discourse in public health and preventive medicine frames non-communicable diseases, including cardiovascular and metabolic diseases, as diseases of ‘lifestyle’; the choice of terminology implies that their prevention, control and management are amenable to individual action. In drawing attention to global increases in the incidence and prevalence of non-communicable disease, however, we increasingly observe that these are non-communicable diseases of poverty. In this article, we call for the reframing of discourse to emphasize the underlying social and commercial determinants of health, including poverty and the manipulation of food markets. We demonstrate this by analysing trends in disease, which indicate that diabetes- and cardiovascular-related DALYS and deaths are increasing particularly in countries categorized as low-middle to middle levels of development. In contrast, countries with very low levels of development contribute least to diabetes and document low levels of CVDs. Although this might suggest that NCDs track increased national wealth, the metrics obscure the ways in which the populations most affected by these diseases are among the poorest in many countries, and hence, disease incidence is a marker of poverty not wealth. We also illustrate variations in five countries — Mexico, Brazil, South Africa, India and Nigeria — by gender, and argue that these differences are associated with gender norms that vary by context rather than sex-specific biological pathways. We tie these trends to shifts in food consumption from whole foods to ultra-processed foods, under colonialism and with continued globalization. Industrialization and the manipulation of global food markets influence food choice in the context of limited household income, time, and household and community resources. Other factors that constitute risk factors for NCDs are likewise constrained by low household income and the poverty of the environment for people with low income, including the capacity of individuals in sedentary occupations to engage in physical activity. These contextual factors highlight extremely limited personal power over diet and exercise. In acknowledging the importance of poverty in shaping diet and activity, we argue the merit in using the term non-communicable diseases of poverty and the acronym NCDP. In doing so, we call for greater attention and interventions to address structural determinants of NCDs.

Rajeev Ranjan Singh, Anjali Sharma, Sanjay K. Mohanty

Abstract Background Though over three-fourths of all births receive medical attention in India, the rate of cesarean delivery (22%) is twice higher than the WHO recommended level. Cesarean deliveries entail high costs and may lead to financial catastrophe for households. This paper examines the out-of-pocket expenditure (OOPE) and distress financing of cesarean deliveries in India. Methods We used data from the latest round of the National Family Health Survey conducted during 2019–21. The survey covered 636,699 households, and 724,115 women in the age group 15–49 years. We have used 159,643 births those delivered three years preceding the survey for whom the question on cost was canvassed. Descriptive analysis, bivariate analysis, concentration index (CI), and concentration curve (CC) were used in the analysis. Result Cesarean deliveries in India was estimated at 14.08%, in private health centres and 9.96%  in public health centres. The prevalence of cesarean delivery increases with age, educational attainment, wealth quintile, BMI and high for those who had pregnancy complications, and previous birth as cesarean. The OOPE on cesarean births was US$133. It was US$498 in private health centres and US$99 in public health centres. The extent of distress financing of any cesarean delivery was 15.37%; 27% for those who delivered in private health centres compared to 16.61% for those who delivered in public health centres. The odds of financial distress arising due to OOPE on cesarean delivery increased with the increase of OOPE [AOR:10.00, 95% CI, 9.35–10.70]. Distress financing increased with birth order and was higher among those with low education and those who belonged to lower socioeconomic strata. Conclusion High OOPE on a cesarean delivery leads to distress financing in India. Timely monitoring of pregnancy and providing comprehensive pregnancy care, improving the quality of primary health centres to conduct cesarean deliveries, and regulating private health centres may reduce the high OOPE and financial distress due to cesarean deliveries in India.

Dzifa Dordunoo, P. Abernethy, Jenipher Kayuni et al.

In this era of confronting racism in public space, it is critical to keep addressing the covert systemic racism in the healthcare system. We want to bring attention to the continued unscientific practice of race-based medicine and the absurdity of treating race as a biological indicator in the 21st century. We believe race is a social construct that does not qualify as a scientific biological indicator for predicting health outcomes. In this paper, we first present arguments for inappropriate use of race in health research and then discuss alternative explanations for health disparity findings that use race as a predictor. Our main concern centers on two specific aspects of the concept of "race": (1) its fundamental lack of scientific basis as a predictor for health outcomes, (2) the misguided narrative that the term creates, placing the onus of racial discrimination on the victim, instead of highlighting the act of discrimination and the role researchers play in actively reinforcing racism when using "race" as a variable. We conclude by proposing that "race" be replaced by the variable "racism" in health.

S. Ofori-Parku, Sung Eun Park

Substandard and falsified medicine (SFM) sales (an estimated > $200 billion) has become one of the worlds’ fastest growing criminal enterprises. It presents an enormous public health and safety challenge. While the developed world is not precluded from this challenge, studies focus on low-income countries. They emphasize supply chain processes, technological, and legal mechanisms, paying less attention to consumer judgment and decision-making aspects. With attention to the demand side of the counterfeit medicines challenge, this survey of U.S. consumers (n = 427) sheds light on some of the social, psychological, and normative factors that underlie consumers’ attitudes, risk perceptions, and purchase intentions. Consumers who (a) self-report that they know about the problem, (b) are older, (c) view counterfeit medicine consumption as ethical, and (d) think their significant others would approve of them using such products are more inclined to perceive lower risks and have favorable purchase intentions. Risk averseness is also inversely related to the predicted outcomes. Perceived benefit of SFMs is a factor but has no effect when risk perception and aversion, attitudes, and subjective norms are factored into the model that predicts purchase intentions. The results of this study indicate that consumer knowledge (albeit in an unexpected direction), people’s expectations about what will impress their significant others, their ethical judgments about selling and consuming counterfeits, and their risk-aversion are associated with their decision-making about counterfeit medicines. The study offers insights into a demand-side approach to addressing SFM consumption in the U.S. Implications for public health, consumer safety, and brand advocacy education are discussed.

Shaiban Ahmed, David Le, Taeyoon Son et al.

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scans with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The five-input channels implementation was observed as the optimal mode for ADC-Net training to achieve robust dispersion compensation in OCT

Colin Megill, Elizabeth Barry, Christopher Small

Systems for large scale deliberation have resolved polarized issues and shifted agenda setting into the public's hands. These systems integrate bridging-based ranking algorithms - including group informed consensus implemented in Polis and the continuous matrix factorization approach implemented by Twitter Birdwatch - making it possible to highlight statements which enjoy broad support from a diversity of opinion groups. Polis has been productively employed to foster more constructive political deliberation at nation scale in law making exercises. Twitter Birdwatch is implemented with the intention of addressing misinformation in the global public square. From one perspective, Twitter Birdwatch can be viewed as an anti-misinformation system which has deliberative aspects. But it can also be viewed as a first step towards a generalized deliberative system, using Twitter's misinformation problem as a proving ground. In this paper, we propose that Twitter could adapt Birdwatch to produce maps of public opinion. We describe a system in five parts for generalizing Birdwatch: activation of a deliberative system and topic selection, population sampling and the role of expert networks, deliberation, reporting interpretable results and finally distribution of the results to the public and those in power.

Avi Cohen, Michal Feldman, Divyarthi Mohan et al.

In the interdependent values (IDV) model introduced by Milgrom and Weber [1982], agents have private signals that capture their information about different social alternatives, and the valuation of every agent is a function of all agent signals. While interdependence has been mainly studied for auctions, it is extremely relevant for a large variety of social choice settings, including the canonical setting of public projects. The IDV model is very challenging relative to standard independent private values, and welfare guarantees have been achieved through two alternative conditions known as {\em single-crossing} and {\em submodularity over signals (SOS)}. In either case, the existing theory falls short of solving the public projects setting. Our contribution is twofold: (i) We give a workable characterization of truthfulness for IDV public projects for the largest class of valuations for which such a characterization exists, and term this class \emph{decomposable valuations}; (ii) We provide possibility and impossibility results for welfare approximation in public projects with SOS valuations. Our main impossibility result is that, in contrast to auctions, no universally truthful mechanism performs better for public projects with SOS valuations than choosing a project at random. Our main positive result applies to {\em excludable} public projects with SOS, for which we establish a constant factor approximation similar to auctions. Our results suggest that exclusion may be a key tool for achieving welfare guarantees in the IDV model.