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Diagnostics (Basel, Switzerland) May 2024This investigation sought to discern the risk factors for atrial fibrillation within Shanghai's Chongming District, analyzing data from 678 patients treated at a...
This investigation sought to discern the risk factors for atrial fibrillation within Shanghai's Chongming District, analyzing data from 678 patients treated at a tertiary hospital in Chongming District, Shanghai, from 2020 to 2023, collecting information on season, C-reactive protein, hypertension, platelets, and other relevant indicators. The researchers introduced a novel dual feature-selection methodology, combining hierarchical clustering with Fisher scores (HC-MFS), to benchmark against four established methods. Through the training of five classification models on a designated dataset, the most effective model was chosen for method performance evaluation, with validation confirmed by test set scores. Impressively, the HC-MFS approach achieved the highest accuracy and the lowest root mean square error in the classification model, at 0.9118 and 0.2970, respectively. This provides a higher performance compared to existing methods, thanks to the combination and interaction of the two methods, which improves the quality of the feature subset. The research identified seasonal changes that were strongly associated with atrial fibrillation (pr = 0.31, FS = 0.11, and DCFS = 0.33, ranked first in terms of correlation); LDL cholesterol, total cholesterol, C-reactive protein, and platelet count, which are associated with inflammatory response and coronary heart disease, also indirectly contribute to atrial fibrillation and are risk factors for AF. Conclusively, this study advocates that machine-learning models can significantly aid clinicians in diagnosing individuals predisposed to atrial fibrillation, which shows a strong correlation with both pathological and climatic elements, especially seasonal variations, in the Chongming District.
PubMed: 38893671
DOI: 10.3390/diagnostics14111145 -
Npj Mental Health Research Jun 2024Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous...
Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.
PubMed: 38890545
DOI: 10.1038/s44184-024-00074-x -
Science and Engineering Ethics Jun 2024Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons...
Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine's behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today's AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call "decision ownership": they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.
Topics: Humans; Artificial Intelligence; Decision Making; Social Responsibility; Decision Support Techniques; Judgment; Machine Learning; Ownership; Robotics
PubMed: 38888795
DOI: 10.1007/s11948-024-00485-1 -
Journal of Nutrition Education and... Jun 2024To gather knowledge and experiences from Squamish Nation citizens to codevelop a model of foraging walks for Indigenous women's heart health.
OBJECTIVE
To gather knowledge and experiences from Squamish Nation citizens to codevelop a model of foraging walks for Indigenous women's heart health.
DESIGN
Qualitative study (sharing circles).
SETTING
Vancouver, Canada (virtual).
PARTICIPANTS
Squamish Nation community members (n = 9), Elders or Knowledge Keepers (n = 5), and researchers (n = 2).
INTERVENTION
Community-led foraging walks as a culturally safe nutrition education strategy.
MAIN OUTCOME MEASURE(S)
Perspectives and experiences.
ANALYSIS
Content analysis and narrative synthesis.
RESULTS
Personal experiences of foraging walks or knowledge of traditional plants were limited for most participants, and all desired to learn more about traditional foods using land-based activities. Participants identified a lack of nutrition education surrounding heart health and common mistreatment and judgment from health professionals. Participants identified important elements of a future Squamish program, including who should be involved, how to implement it, and the most effective temporal and physical setting. All agreed foraging walks help promote 5 dimensions of heart health (physical, emotional, spiritual, mental, and social) through physical activity, purposeful nutrition, and connection to community and culture. Findings from the sharing circles were used in the creation of a template for future foraging sessions and contributed to plant identification cards for the whole community.
CONCLUSIONS AND IMPLICATIONS
Community-based pilot studies to test foraging walks as a culturally safe and environmental approach to nutrition education and cardiovascular health awareness for Indigenous communities are warranted. Research to examine the similarities and differences across Indigenous groups related to understanding heart health and land-based practices for nutrition education and heart health awareness is needed.
PubMed: 38888537
DOI: 10.1016/j.jneb.2024.04.003 -
Frontiers in Psychology 2024In this study, we aimed to characterize the affordance of interceptability for oneself using a manual lateral interception paradigm. We asked a two-fold research...
In this study, we aimed to characterize the affordance of interceptability for oneself using a manual lateral interception paradigm. We asked a two-fold research question: (1) What makes a virtual ball interceptable or not? (2) How reliably can individuals perceive this affordance for oneself? We hypothesized that a spatiotemporal boundary would determine the interceptability of a ball, and that individuals would be able to perceive this boundary and make accurate perceptual judgments regarding their own interceptability. To test our hypotheses, we administered a manual lateral interception task to 15 subjects. They were first trained on the task, which was followed by two experimental sessions: and . In the former, participants were instructed to intercept as many virtual balls as possible using a hand-held slider to control an on-screen paddle. In the latter session, while making interceptions, participants were instructed to call "no" as soon as they perceived a ball to be uninterceptable. Using generalized linear modeling on the data, we found a handful of factors that best characterized the affordance of interceptability. As hypothesized, distance to be covered and ball flight time shaped the boundary between interceptable and uninterceptable balls. Surprisingly, the angle of approach of the ball also co-determined interceptability. Altogether, these variables characterized the actualized interceptability. Secondly, participants accurately perceived their own ability to intercept balls on over 75% of trials, thus supporting our hypothesis on perceived interceptability. Analyses revealed that participants considered this action boundary while making their perceptual judgments. Our results imply that the perceiving and actualizing of interceptability are characterized by a combination of the same set of variables.
PubMed: 38882508
DOI: 10.3389/fpsyg.2024.1397476 -
Frontiers in Neuroscience 2024In cognitive behavioral experiments, we often asked participants to make judgments within a deadline. However, the most common instruction of "do the task quickly and...
INTRODUCTION
In cognitive behavioral experiments, we often asked participants to make judgments within a deadline. However, the most common instruction of "do the task quickly and accurately" does not highlight the importance of the balance between being fast and accurate.
METHODS
Our research aimed to explore how instructions about speed or accuracy affect perceptual process, focus on event-related potentials (ERPs) and event-related oscillations (EROs) of two brain responses for visual stimuli, known as P1 and N1. Additionally, we compared the conventional analysis approach with principal component analysis (PCA) based methods to analyze P1 and N1 ERP amplitude and ERO power.
RESULTS
The results showed that individuals instructed to respond quickly had lower P1 amplitude and alpha ERO than those who prioritized accuracy, using the PCA-based approach. However, these two groups had no differences between groups in the N1 theta band using both methods. The traditional time-frequency analysis method could not detect any ERP or ERO distinctions between groups due to limitations in detecting specific components in time or frequency domains. That means PCA is effective in separating these components.
DISCUSSION
Our findings indicate that the instructions given regarding speed and accuracy impact perceptual process of subjects during cognitive behavioral experiments. We suggest that future researchers should choose their instructions carefully, considering the purpose of study.
PubMed: 38881749
DOI: 10.3389/fnins.2024.1354051 -
Archives of Dermatological Research Jun 2024There are many therapeutic modalities for plantar warts, however treating it remains challenging. Intralesional injection of 5-fluorouarcil and combined digoxin and... (Randomized Controlled Trial)
Randomized Controlled Trial Comparative Study
There are many therapeutic modalities for plantar warts, however treating it remains challenging. Intralesional injection of 5-fluorouarcil and combined digoxin and furosemide were observed to be effective and safe, however no comparison study between them was done. Our study was conducted to evaluate the efficacy of both therapies in the treatment of plantar warts. 90 adult patients with multiple recalcitrant plantar warts were included in our study. They were randomly allocated to one of three groups; combined digoxin and furosemide, 5-fluorouarcil, or normal saline group. Fortnightly injections were done into all studied warts till complete clearance or up to 5 sessions. Warts were evaluated clinically and dermoscopically. Clinical response was reported in 24 patients (80%) of the combined digoxin and furosemide group with 40% complete response and in 24 patients (80%) of the 5-fluorouarcil group with 33.3% complete response. No statistically significant difference was observed between the two groups concerning efficacy and safety. Intralesional injection of 5-fluorouarcil and combined digoxin and furosemide are nearly equivalent in efficacy and safety for plantar wart treatment. Dermoscopy helps to take the truthful judgment about complete clearance of warts.
Topics: Humans; Furosemide; Male; Female; Adult; Warts; Digoxin; Injections, Intralesional; Treatment Outcome; Prospective Studies; Young Adult; Middle Aged; Drug Therapy, Combination; Adolescent; Dermoscopy; Flucytosine
PubMed: 38878078
DOI: 10.1007/s00403-024-03014-z -
JMIR AI Jun 2024Clinical decision-making is a crucial aspect of health care, involving the balanced integration of scientific evidence, clinical judgment, ethical considerations, and...
Clinical decision-making is a crucial aspect of health care, involving the balanced integration of scientific evidence, clinical judgment, ethical considerations, and patient involvement. This process is dynamic and multifaceted, relying on clinicians' knowledge, experience, and intuitive understanding to achieve optimal patient outcomes through informed, evidence-based choices. The advent of generative artificial intelligence (AI) presents a revolutionary opportunity in clinical decision-making. AI's advanced data analysis and pattern recognition capabilities can significantly enhance the diagnosis and treatment of diseases, processing vast medical data to identify patterns, tailor treatments, predict disease progression, and aid in proactive patient management. However, the incorporation of AI into clinical decision-making raises concerns regarding the reliability and accuracy of AI-generated insights. To address these concerns, 11 "verification paradigms" are proposed in this paper, with each paradigm being a unique method to verify the evidence-based nature of AI in clinical decision-making. This paper also frames the concept of "clinically explainable, fair, and responsible, clinician-, expert-, and patient-in-the-loop AI." This model focuses on ensuring AI's comprehensibility, collaborative nature, and ethical grounding, advocating for AI to serve as an augmentative tool, with its decision-making processes being transparent and understandable to clinicians and patients. The integration of AI should enhance, not replace, the clinician's judgment and should involve continuous learning and adaptation based on real-world outcomes and ethical and legal compliance. In conclusion, while generative AI holds immense promise in enhancing clinical decision-making, it is essential to ensure that it produces evidence-based, reliable, and impactful knowledge. Using the outlined paradigms and approaches can help the medical and patient communities harness AI's potential while maintaining high patient care standards.
PubMed: 38875592
DOI: 10.2196/55957 -
JMIR AI Apr 2024The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The...
BACKGROUND
The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles.
OBJECTIVE
This study aims to determine the impact of AI on the management and practices of Qatar's Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians' job roles, including associated risks and ethical ramifications from their perspective.
METHODS
We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses.
RESULTS
Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians.
CONCLUSIONS
This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians' roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.
PubMed: 38875531
DOI: 10.2196/40781 -
Frontiers in Immunology 2024To evaluate the methodological quality, report quality, and evidence quality of meta-analysis (MA) and systematic review (SR) on the efficacy of probiotics in the...
BACKGROUND
To evaluate the methodological quality, report quality, and evidence quality of meta-analysis (MA) and systematic review (SR) on the efficacy of probiotics in the treatment of rheumatoid arthritis (RA).
METHODS
Databases were used to identify eligible SRs/MAs until February 12, 2024. The methodological quality of the studies was assessed using AMSTAR-2 tool, the quality of the literature reports was scored using PRISMA checklists, and the quality of the evidence was graded using GRADE system.
RESULTS
Seven reviews including 21 outcomes were included. Methodological quality of the included reviews was of general low, and the entries with poor scores were 2, 4, and 7. By PRISMA checklists, there were some reporting deficiencies, and quality problems were mainly reflected in the reporting registration and protocol, comprehensive search strategy and additional analysis. GRADE results elevated the quality of evidence to be low or very low overall.
CONCLUSIONS
Probiotics may have a therapeutic effect on RA, based on the evidence provided by the SRs/MAs in this overview. Nevertheless, there is still a lack of conclusive evidence due to methodological limitations in the included research. To make trustworthy judgments regarding the efficacy of probiotics in the treatment of RA, more large-scale, high-quality randomized controlled trials are still required.
Topics: Probiotics; Arthritis, Rheumatoid; Humans; Systematic Reviews as Topic; Treatment Outcome; Meta-Analysis as Topic
PubMed: 38873605
DOI: 10.3389/fimmu.2024.1397716