Science topic
Bias (Epidemiology) - Science topic
Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.
Publications related to Bias (Epidemiology) (10,000)
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The integration of Artificial Intelligence (AI) in education is rapidly transforming the teaching and learning paradigm. Recent systematic reviews have revealed a surge in research examining the efficacy, implementation, and potentialities associated with AI in education. This trend reflects a growing acknowledgment of its capacity to revolutionize...
Accurate stress detection from physiological signals is often complicated by individual identity traits, which must first be identified before they can be effectively removed to improve model performance. To address this, we propose a method that combines Detrended Fluctuation Analysis (DFA) and Augmented Dickey-Fuller (ADF) Analysis to extract sta...
In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in dec...
This study presents a novel data-driven approach for generating spectrum-matched earthquake ground motions using physics-informed neural networks (PINNs). The methodology leverages real recorded earthquake data and employs singular value decomposition for dimensionality reduction, enabling the extraction of eigen motions that capture correlated tem...
Artificial intelligence (AI) has profoundly transformed numerous facets of both private and professional life. Understanding how people evaluate AI is crucial for predicting its future adoption and addressing potential barriers. However, existing instruments measuring attitudes towards AI often focus on specific technologies or cross-domain evaluat...
The chapter discusses the benefits of utilising AI in education fields. It describes the various types of AI technologies used in education, such as teacher assistance tools, adaptive student assistance programs, and natural language processing, and reveals the use of virtual reality. This chapter provides a detailed layout of the advantages and di...
This chapter focuses on the intricacies and future possibilities of coordinating computerised reasoning (artificial intelligence) innovations in the advanced education scene. It investigates the multi-layered difficulties faced by establishments, instructors, and partners in utilising artificial intelligence to upgrade education, learning, and regu...
Machine unlearning is an emerging field in machine learning that focuses on efficiently removing the influence of specific data from a trained model. This capability is critical in scenarios requiring compliance with data privacy regulations or when erroneous data needs to be removed without retraining from scratch. In this study, I explore the imp...
The integration of artificial intelligence (AI) in healthcare systems has transformed patient care, diagnostics, and operational efficiency. However, its deployment in high-stakes environments raises ethical, practical, and technological challenges. This paper explores the multifaceted impact of human-AI collaboration in healthcare, focusing on iss...
Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and metho...
Mediation models are often conducted in psychology to understand mechanisms and processes of change. However, current best practices for handling missing data in mediation models are not always used by researchers. Missing data methods, such as full information maximum likelihood (FIML) and multiple imputation (MI), are best practice methods of han...
Depression is a frequent and dangerous medical disorder that has an unhealthy effect on how a person feels, thinks, and acts. Depression is also quite prevalent. Early detection and treatment of depression may avoid painful and perhaps life-threatening symptoms. An imbalance in the data creates several challenges. Consequently, the majority learner...
Seaport efficiency measurement is one of the most popular topics in maritime economics. Studies within this research area have not paid attention to the well-known simultaneity bias in productivity and efficiency measurement that can lead to inconsistent estimates of best practices. This paper investigates simultaneity in seaport efficiency measure...
We investigate the effect of exposure to air pollution on entrepreneurship using panel data from the China Health and Retirement Longitudinal Study. To address endogeneity arising from location choices and omitted variable bias, we employ a two-way fixed effects model with an instrumental variable approach. We find that adults exposed to high level...
Artificial Intelligence (AI) models, particularly Natural Language Processing (NLP) frameworks, have made remarkable strides in automating text summarization tasks. Despite their progress, these models encounter significant limitations when tasked with summarizing complex, nuanced, and domain-specific texts. This research explores the challenges fa...
The peer review process is essential for academic research, yet it faces challenges such as inefficiencies, biases, and limited access to qualified reviewers. This paper introduces an autonomous peer reviewer selection system that employs the Natural Language Processing (NLP) model to match submitted papers with expert reviewers independently of tr...
The present study explores the paradoxical relationship between empathy and loneliness, hypothesising that high empathy may exacerbate loneliness through the intentionality bias. While empathy is typically associated with positive social connections, recent research suggests that over-empathising may lead to loneliness by increasing susceptibility...
Previous studies have reported fewer social biases in bilinguals compared to monolinguals. However, it is unclear whether the expression of social biases varies across the bilingualism spectrum. This article investigates the connections between different dimensions of bilingual experience and the expression of explicit bias. We analyzed the respons...
The relationship between perception and language in Merleau-Ponty’s works will here be explored in
detail, leading to the conclusion that he integrates them and does not exclusively feature one over the
other, as is frequently claimed. We will see that the issue of the relationship between perception and
language is connected to the relationship be...
Additional efforts are necessary to guarantee that the machine learning algorithms employed in healthcare do not perpetuate or exacerbate any preexisting discriminatory or objectionable biases that may be present in the data. In order to reduce the impact of any biases that may have developed during the data collection procedure, we implement a rei...
We investigate how the computational difficulty of contracting tensor networks depends on the sign structure of the tensor entries. Using results from computational complexity, we observe that the approximate contraction of tensor networks with only positive entries has lower computational complexity as compared to tensor networks with general real...
This paper introduces a novel robust Kalman filter designed to leverage symmetrical properties within the Pearson Type VII-Inverse Wishart (PVIW) distribution, enhancing state estimation accuracy in the presence of time-varying biases and non-stationary heavy-tailed (NSHT) noise. The filter includes a shape parameter from the normal distribution an...
Climate change-induced compound extreme events pose significant challenges, causing widespread damage and threatening lives. Climate models are important tools for analyzing and predicting such complex events. Some recently released Phase 6 Coupled Model Intercomparison Program (CMIP6) Global Climate Models (GCMs) have undergone improvements in res...
Background
Maximum oxygen consumption is a measure of an individual’s cardiorespiratory fitness which is a singular predictor of an array of diseases. Several exercise and non-exercise assessments are frequently compared to know which method(s) provide the most accurate estimation of aerobic capacity due to difficulties in using the direct method....
There is an ongoing need to identify novel pharmacological agents for the effective treatment of depression. One emerging candidate, which has demonstrated rapid-acting antidepressant effects in treatment-resistant groups, is nitrous oxide (N 2 O)—a gas commonly used for sedation and pain management in clinical settings and with a range of pharmaco...
This paper explores the critical role of ethical principles in computer forensics, focusing on confidentiality, integrity, objectivity, competence, and legal compliance. These principles are essential for ensuring that forensic investigations are conducted with respect for individual rights and legal standards, thereby upholding the credibility and...
Purpose
To evaluate the predictive accuracy of 11 intraocular lens (IOL) calculation formulas in eyes with an axial length (AL) less than 22.00 mm.
Methods
New-generation formulas (Barrett Universal II [BUII], Emmetropia Verifying Optical [EVO] 2.0, Hill-Radial Basis Function [Hill-RBF] 3.0, Hoffer QST, K6, Kane, Pearl-DGS) and traditional formula...
The information transfer necessary for successful memory retrieval is believed to be mediated by theta and gamma oscillations. These oscillations have been linked to memory processes in electrophysiological studies, which were correlational in nature. In the current study, we used transcranial alternating current stimulation (tACS) to externally mo...
This paper breaks away from traditional approaches that merely emulate digital neural networks. Using Mach-Zehnder interferometer (MZI) networks as a case study, we explore the impact of the inherent properties of analog computation on performance and identify the characteristics that optical neural networks (ONNs) components should possess to bett...
Perceptions of the healthiness of Black women shape the way that they are treated and may differ by characteristics of the person and the perceiver. We examined perceptions of Black women’s physical healthiness by skin tone and rater race. In a within-subjects design, adults (N = 280; 45.7% Black, 54.3% White) rated the physical healthiness of the...
Observational learning enables us to make decisions by watching others’ behaviors. The quality of such learning depends on the abilities of those we observe, but also on our beliefs about those abilities. We have previously demonstrated that observers learned better from demonstrators described as high vs. low in ability, regardless of their actual...
Introduction
Interventional single‐arm trials (SATs) are increasingly being used as evidence, despite a lack of agreement on their validity and where they should sit in the hierarchy of evidence. We conducted a meta‐epidemiological study to investigate whether there are systematic differences in outcomes and levels of between‐study heterogeneity fo...
Purpose
There is a consistent link between perfectionism and compulsive exercise, and both are implicated in the maintenance of eating disorders, however no meta-analysis to date has quantified this relationship. We hypothesised that there would be significant, small-moderate pooled correlations between perfectionism dimensions and compulsive exerc...
Muslim parents in the U.S. are expected to engage in socialization practices to promote cultural pride and prepare their children for experiences with discrimination based on their racialized religious identity. However, very little is known about these practices and how such practices relate to youths’ Muslim identity. U.S. Muslims between 16 and...
Impression formation is a dynamic process which individuals will update others’ impressions as the targets’ behaviors or traits change. Many past studies have found a negative bias effect on the moral impression updating. This study aimed to explore how the direction of change affected moral impression updating. Study 1 investigated the impression...
Extensive research work has been done for the estimation of population mean using bivariate auxiliary information based on conventional measures. Conventional measures of the auxiliary variables provide suspicious results in the presence of outliers/extreme values. However, non-conventional measures of the auxiliary variables include quartile devia...
We address the challenges of Byzantine-robust training in asynchronous distributed machine learning systems, aiming to enhance efficiency amid massive parallelization and heterogeneous computing resources. Asynchronous systems, marked by independently operating workers and intermittent updates, uniquely struggle with maintaining integrity against B...
Aims The aims of this study were (i) to develop an automatic system capable of calculating four radiographic measurements used in the diagnosis and monitoring of cerebral palsy (CP) related hip disease, and (ii) to demonstrate that these measurements are sufficiently accurate to be used in clinical practice. Methods We developed a machine-learning...
In the context of global warming, the frequency and intensity of extreme temperature and precipitation events are increasing. Under this scenario, an increase in compound extreme events would pose a greater risk to human society and ecosystem. However, the modelling and future projection of various types of compound events remain a great challenge....
Background: Mouth opening is significant for diagnosing many clinical conditions. Therefore, it becomes essential to establish normal maximum
mouth opening (MMO). Aim: To study role of BMI while evaluating MMO using three‑finger index. Materials and Method: A total of 500
subjects between 20 and 60 years were considered. Thorough clinical examinati...
This analysis article aimed to identify and analyze all articles published on the post-COVID-19 condition in Latin America and the Caribbean, focusing on epidemiology, clinical characteristics, and risk of bias. We did a systematic survey of the literature with broad inclusion criteria. The only exclusion criteria were articles referring to post-ac...
Gender-neutral language reflects societal and linguistic shifts towards greater inclusivity by avoiding the implication that one gender is the norm over others. This is particularly relevant for grammatical gender languages, which heavily encode the gender of terms for human referents and over-relies on masculine forms, even when gender is unspecif...
![Comparative Analysis of AI Ethics Performance Metrics Across Sectors [7, 8]](profile/Rishi-Kumar-Sharma-4/publication/388062868/figure/tbl1/AS:11431281303847862@1737024813422/Comparative-Analysis-of-AI-Ethics-Performance-Metrics-Across-Sectors-7-8_Q320.jpg)
![Progress in Ethical AI Implementation Dimensions (2021-2024) [9, 10]](profile/Rishi-Kumar-Sharma-4/publication/388062868/figure/tbl2/AS:11431281303810180@1737024813550/Progress-in-Ethical-AI-Implementation-Dimensions-2021-2024-9-10_Q320.jpg)
The rapid advancement of artificial intelligence technologies has created unprecedented opportunities while raising significant ethical concerns across various sectors. This comprehensive article examines the challenges and strategies in implementing ethical AI frameworks, focusing on algorithmic bias, transparency, and accountability. The article...
A new compound [Y2(sq)3(H2O)4] (Y-sq; sq = squarate (C4O42–)) was prepared and structurally characterized. Since the RE-sq family (RE = Y, Dy, Yb, Lu) gave isostructural crystals, the objective of this study is to explore the effects of diamagnetic dilution on the SIM behavior through systematic investigation and comparison of diamagnetically dilut...
Physics-informed neural networks (PINNs) have prevailed as differentiable simulators to investigate flow in porous media. Despite recent progress PINNs have achieved, practical geotechnical scenarios cannot be readily simulated because conventional PINNs fail in discontinuous heterogeneous porous media or multi-layer strata when labeled data are mi...
Intrinsic structural and oxidic defects activate graphitic carbon electrodes towards electrochemical reactions underpinning energy conversion and storage technologies. Yet, these defects can also disrupt the long‐range and periodic arrangement of carbon atoms, thus, the characterization of graphitic carbon electrodes necessitates in‐situ atomistic...
Artificial Intelligence (AI) is revolutionizing industries, and the legal field is no exception. This research explores the integration of AI into legal systems, focusing on regulatory adaptation and ethical considerations. It investigates how AI applications, such as legal research tools, predictive analytics, and automated document generation, ar...
Mutation rates drive the pace and potential of evolutionary change. However, to better understand the evolutionary implications of mutation rates, there is a need to uncover the causes of their diversfification. In multicellular organisms, all mutations first arise in a single cell in a developmental context. Whether a mutation enters a population'...
Haiyang 2B (HY-2B), the second Chinese ocean dynamic environment monitoring satellite, has been operational for nearly six years. The scanning microwave radiometer (SMR) onboard HY-2B provides global sea surface temperature (SST) observations. Comprehensive validation of these data is essential before they can be effectively applied. This study eva...
In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditiona...
The early detection of Alzheimer's disease (AD) is critical for timely interventions and improved patient outcomes. Machine learning (ML) has emerged as a transformative tool in this domain, capable of analyzing complex datasets to identify subtle patterns indicative of early AD. However, several challenges hinder its widespread application, includ...
Background
The current evaluation of surgical resident operative autonomy consists primarily of self-report and is prone to bias. Objective performance indicators (OPIs) generated from the da Vinci Surgical System capture objective intraoperative data providing an opportunity to evaluate the intraoperative resident experience more accurately. This...
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work investigating the impact of multitask learning on performance and fairness in depression detection nor leveraged it to achieve fairer...
The potential for an individual's social partners to buffer--or otherwise modify--how individuals respond to their environment has been demonstrated to be important in many contexts. This buffering has the potential to affect responses to human modifications of environments. Unfortunately, statistical tools for identifying buffering effects have no...
This study aims to evaluate the impact of lateral boundary conditions, land surface model schemes, and soil conditions on the simulations during extreme heat events in Metro Manila, Philippines. Extreme heat events are simulated using a numerical model representing urban land use and anthropogenic heat flux and compared with observation data. The s...
The success of VLMs often relies on the dynamic high-resolution schema that adaptively augments the input images to multiple crops, so that the details of the images can be retained. However, such approaches result in a large number of redundant visual tokens, thus significantly reducing the efficiency of the VLMs. To improve the VLMs' efficiency w...
This article describes an assignment used in a 100-level college world history class that requires students to produce history essays using ChatGPT and then annotate and assess those essays according to how well they analyze topics covered in the course. The article first demonstrates how the assignment has proved a useful tool in promoting student...
Outlier values and rankings are important for emphasizing data distribution variability, which improves the accuracy and effectiveness of variance estimations. To enhance the estimation of finite population variance in a two-phase sampling framework, this study presents an improved class of double exponential-type estimators by utilizing the outlie...
The paper focuses on two major transitions that the meteorological observational system in India has undergone. The first was in the late 1870s when thermometer sheds were set up at Indian observatories, and the second in the 1920s when there was a changeover from thermometer sheds to Stevenson screens. The paper expresses the need to investigate p...
Concentrations of pollutants like pharmaceuticals in soils typically decrease over time, though it often remains unclear whether this dissipation is caused by the transformation of the pollutant or a decreasing extractability. We developed a mathematical model that (1) explores the plausibility of different dissipation pathways, and (2) allows the...
Exposure-response (ER) analyses are routinely performed as part of model-informed drug development to evaluate the risk-to-benefit ratio for dose selection, justification, and confirmation. For logistic regression analyses with binary endpoints, several exposure metrics are investigated, based on pharmacological plausibility, including time-average...
This study aimed to evaluate the potential of Large Language Models (LLMs) in healthcare diagnostics, specifically their ability to analyze symptom-based prompts and provide accurate diagnoses. The study focused on models including GPT-4, GPT-4o, Gemini, o1 Preview, and GPT-3.5, assessing their performance in identifying illnesses based solely on p...
Here we disclose that spiropyrans are able to undergo dynamic covalent exchange via their corresponding merocyanine isomers. In the latter, the indolinium moieties can be exchanged by a Michael‐type addition‐elimination sequence, in which a methylene indoline attacks a merocyanine and subsequently the initial indoline fragment is cleaved. The rate...
Organizations cannot avoid bias despite growing awareness of fair employee recruitment and selection processes. The purpose of this study is to identify the factors that contribute to bias. A recent survey found that 84% of candidates believe companies' recruiting practices are unfair or biased. (Vanderpal & Brazie, 2022). Furthermore, the survey f...
This commentary discusses the transformative role of Artificial Intelligence (AI) in enhancing simulation-based learning in nursing education. It highlights how AI-driven simulations provide realistic and interactive training environments crucial for developing nursing competencies. The commentary also explores challenges such as potential over-rel...
This study reported the prediction model of heating value for the Acacia mangium Willd which is the promoted as energy plant for the farmer to grow in the farm. Its heating value is approximately of 19 kJ/kg which provides high potential using for an alternative energy as biomass. The near infrared spectroscopy technique (NIR) is used to create mod...
We conduct a comprehensive study into the impact of pixelization on cosmic shear, uncovering several sources of bias in standard pseudo-$C_\ell$ estimators based on discrete catalogues. We derive models that can bring residual biases to the percent level on small scales. We elucidate the impact of aliasing and the varying shape of HEALPix pixels on...
