Hasil untuk "Medicine"

Carsten Lexa

This chapter presents an assessment of the rapidly evolving state of health-related technology and its developing impact on health care, medical education, patient care, and care delivery. This is collectively referred to as the digital health movement in medicine. This chapter provides a broader understanding of how digital health is changing not only the practice of medicine, but the consumer market that pertains to health care and medicine at large. The authors discuss the current state of digital health in medicine, the challenges of conventionally assessing digital health-related competencies, and the relative difficulty of adapting contemporary medical education to include digital health modalities into traditional undergraduate medical education. This chapter also showcases three unique case studies of early-adopting medical institutions that have created digital health learning opportunities for their undergraduate medical student population.

M. DeJonckheere, L. Vaughn

Semistructured in-depth interviews are commonly used in qualitative research and are the most frequent qualitative data source in health services research. This method typically consists of a dialogue between researcher and participant, guided by a flexible interview protocol and supplemented by follow-up questions, probes and comments. The method allows the researcher to collect open-ended data, to explore participant thoughts, feelings and beliefs about a particular topic and to delve deeply into personal and sometimes sensitive issues. The purpose of this article was to identify and describe the essential skills to designing and conducting semistructured interviews in family medicine and primary care research settings. We reviewed the literature on semistructured interviewing to identify key skills and components for using this method in family medicine and primary care research settings. Overall, semistructured interviewing requires both a relational focus and practice in the skills of facilitation. Skills include: (1) determining the purpose and scope of the study; (2) identifying participants; (3) considering ethical issues; (4) planning logistical aspects; (5) developing the interview guide; (6) establishing trust and rapport; (7) conducting the interview; (8) memoing and reflection; (9) analysing the data; (10) demonstrating the trustworthiness of the research; and (11) presenting findings in a paper or report. Semistructured interviews provide an effective and feasible research method for family physicians to conduct in primary care research settings. Researchers using semistructured interviews for data collection should take on a relational focus and consider the skills of interviewing to ensure quality. Semistructured interviewing can be a powerful tool for family physicians, primary care providers and other health services researchers to use to understand the thoughts, beliefs and experiences of individuals. Despite the utility, semistructured interviews can be intimidating and challenging for researchers not familiar with qualitative approaches. In order to elucidate this method, we provide practical guidance for researchers, including novice researchers and those with few resources, to use semistructured interviewing as a data collection strategy. We provide recommendations for the essential steps to follow in order to best implement semistructured interviews in family medicine and primary care research settings.

M. Murad, Noor Asi, Mouaz Alsawas et al.

A pyramid has expressed the idea of hierarchy of medical evidence for so long, that not all evidence is the same. Systematic reviews and meta-analyses have been placed at the top of this pyramid for several good reasons. However, there are several counterarguments to this placement. We suggest another way of looking at the evidence-based medicine pyramid and explain how systematic reviews and meta-analyses are tools for consuming evidence—that is, appraising, synthesising and applying evidence.

S. Kaatz, D. Ahmad, A. Spyropoulos et al.

Rene Y. Choi, Aaron S. Coyner, Jayashree Kalpathy-Cramer et al.

Purpose To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology. Results A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background. Conclusions Artificial intelligence has a promising future in medicine; however, many challenges remain. Translational Relevance The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.

S. Parveen, R. Misra, S. Sahoo

I. Kononenko

M. R. Kumar, R. Muzzarelli, C. Muzzarelli et al.

Andrew Gelman, Amy Krefman, Lauren Kennedy et al.

When designing and evaluating an experiment or observational study, it is useful to have a realistic hypothesis regarding the average treatment effect. We present an approach to conceptualizing this average by first considering a distribution of effects. We demonstrate with examples in medicine, economics, and psychology.

Nghia Nhu Nguyen, Bao The Nguyen, Huyen Thi Ngoc Le et al.

Background Liver fibrosis is a significant health burden in Vietnamese male adults, driven by high rates of hepatitis B and hepatitis C, excessive alcohol consumption, and genetic and environmental factors. Despite progress in diagnostic tools, there is a pressing need for cost-effective screening methods tailored to this high-risk group, particularly in resource-limited settings. Methods This study enrolled 952 Vietnamese male adults over 40 years old undergoing FibroScan, excluding those with conditions affecting test accuracy. Data on demographics, clinical history, and anthropometrics were collected, and fibrosis stages were classified using the METAVIR system. Model development combined Bayesian model averaging and forward stepwise methods, with predictive performance validated via receiver operating characteristic (ROC) analysis and area under the curve (AUC) estimation in the R environment. Results Among 952 male participants, the prevalence of liver fibrosis was 19.9%, with most cases classified as mild (F1). Multivariate analysis identified significant risk factors, including advanced age (odds ratio (OR) = 1.6; 95% confidence interval (CI) [1.02–2.51]), alcohol abuse (OR = 4.44; 95% CI [2.65–7.42]), hepatitis B (OR = 6.76; 95% CI [3.14–14.54], hepatitis C (OR = 33.04; 95% CI [5.26–207.42]), family history of cirrhosis (OR = 16.14; 95% CI [3.28–79.55]), and hepatic steatosis (OR = 4.02; 95% CI [2.57–6.28]). The predictive model demonstrated good discriminative performance with an AUC of 0.769 (95% CI [0.734–0.800]) and showed satisfactory calibration through bootstrap resampling, indicating close agreement between predicted and observed risks. Conclusion The current prevalence of liver fibrosis among Vietnamese male adults was found to be 19.9%, and the developed risk prediction model effectively identifies high-risk individuals, enabling early diagnosis and targeted prevention, particularly in resource-limited settings. However, the lack of external validation and the sample restricted to Vietnamese male adults limit the generalizability of the model, which should be further evaluated in other populations.

Rebecca Nalloor, Rebecca Nalloor, Khadijah Shanazz et al.

IntroductionOnly a subset of people who experience a traumatic event develop Post-Traumatic Stress Disorder (PTSD) suggesting that there are susceptibility factors influencing PTSD pathophysiology. While post trauma sequelae factors are extensively studied, susceptibility factors are difficult to study and therefore poorly understood. To address this gap, we previously developed an animal model - Revealing Individual Susceptibility to PTSD-like phenotype (RISP). RISP allows studying susceptibility factors by identifying, before trauma, male rats that are likely to develop a PTSD-like phenotype after trauma. Hypofunctioning prefrontal cortex (PFC) has been reported in people with PTSD, however, it is unclear if it is a susceptibility factor, sequalae factor, or both. Here we tested the hypothesis that male rats classified as Susceptible with RISP will have altered medial prefrontal cortical (mPFC) function prior to a PTSD-inducing trauma.MethodsExperiment 1: Susceptible and Resilient male rats classified with RISP performed spatial exploration and were sacrificed immediately to assess neuronal expression of plasticity-related immediate early genes (Arc and Homer1a) in the medial PFC (mPFC). Experiment 2: Cognitive performance of Susceptible and Resilient rats was evaluated on an attentional set shifting task. Experiment 3: We also analyzed pre-trauma cognitive performance scores of a small group of male military personnel some of whom developed PTSD post-trauma.ResultsExperiment 1: Susceptible rats showed altered expression of plasticity-related immediate early genes in the Prelimbic and Infralimbic subregions of the mPFC following spatial exploration. Experiment 2: Susceptible rats showed deficits in attentional set shifting task only when task demands increased. Experiment 3: Male military personnel who developed PTSD post-trauma showed pre-trauma cognitive deficits in a task involving the PFC.DiscussionSusceptible rats showed mPFC deficits both at the cellular and behavioral level before PTSD-inducing trauma. Combined with the findings from the human data, these results support the hypothesis that mPFC deficits in males exist before trauma and thus are a putative susceptibility factor for PTSD. Whether these deficits are a bona fide susceptibility factor will be determined in future studies by testing if enhancing mPFC function in susceptible individuals before trauma will confer resilience to developing PTSD. Building resilience is crucial for minimizing the number of people suffering from PTSD, given that it is difficult to treat and treatments are resource intensive and benefit only a subpopulation of people suffering from PTSD.

Zahra Atf, Seyed Amir Ahmad Safavi-Naini, Peter R. Lewis et al.

This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications. As LLMs become integral to clinical decision-making, accurately communicating uncertainty is crucial for ensuring reliable, safe, and ethical AI-assisted healthcare. Our research frames uncertainty not as a barrier but as an essential part of knowledge that invites a dynamic and reflective approach to AI design. By integrating advanced probabilistic methods such as Bayesian inference, deep ensembles, and Monte Carlo dropout with linguistic analysis that computes predictive and semantic entropy, we propose a comprehensive framework that manages both epistemic and aleatoric uncertainties. The framework incorporates surrogate modeling to address limitations of proprietary APIs, multi-source data integration for better context, and dynamic calibration via continual and meta-learning. Explainability is embedded through uncertainty maps and confidence metrics to support user trust and clinical interpretability. Our approach supports transparent and ethical decision-making aligned with Responsible and Reflective AI principles. Philosophically, we advocate accepting controlled ambiguity instead of striving for absolute predictability, recognizing the inherent provisionality of medical knowledge.

Vida Gholamiyan, Yaning Zhao, Wafa Labidi et al.

Molecular communication (MC) is an emerging paradigm that enables data transmission through biochemical signals rather than traditional electromagnetic waves. This approach is particularly promising for environments where conventional wireless communication is impractical, such as within the human body. However, security and privacy pose significant challenges that must be addressed to ensure reliable communication. Moreover, MC is often event-triggered, making it logical to adopt goal-oriented communication strategies, similar to those used in message identification. This work explores secure identification strategies for MC, with a focus on the information-theoretic security of message identification over Poisson wiretap channels (DT-PWC).

Andong Wang, Kelly Wentzlof, Johnny Rajala et al.

Modern precision medicine aims to utilize real-world data to provide the best treatment for an individual patient. An individualized treatment rule (ITR) maps each patient's characteristics to a recommended treatment scheme that maximizes the expected outcome of the patient. A challenge precision medicine faces is population heterogeneity, as studies on treatment effects are often conducted on source populations that differ from the populations of interest in terms of the distribution of patient characteristics. Our research goal is to explore a transfer learning algorithm that aims to address the population heterogeneity problem and obtain targeted, optimal, and interpretable ITRs. The algorithm incorporates a calibrated augmented inverse probability weighting (CAIPW) estimator for the average treatment effect (ATE) and employs value function maximization for the target population using Genetic Algorithm (GA) to produce our desired ITR. To demonstrate its practical utility, we apply this transfer learning algorithm to two large medical databases, Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). We first identify the important covariates, treatment options, and outcomes of interest based on the two databases, and then estimate the optimal linear ITRs for patients with sepsis. Our research introduces and applies new techniques for data fusion to obtain data-driven ITRs that cater to patients' individual medical needs in a population of interest. By emphasizing generalizability and personalized decision-making, this methodology extends its potential application beyond medicine to fields such as marketing, technology, social sciences, and education.

Lara Maleyeff, Shirin Golchi, Erica E. M. Moodie et al.

Precision medicine tailors treatments to individual patient characteristics, which is especially valuable for conditions like obstructive sleep apnea (OSA), where treatment responses vary widely. Traditional trials often overlook subgroup differences, leading to suboptimal recommendations. Current approaches rely on pre-specified thresholds with inherent uncertainty, assuming these thresholds are correct-a flawed assumption. This case study compares pre-specified thresholds to two advanced Bayesian methods: the established FK-BMA method and its novel variant, FK. The FK approach retains the flexibility of free-knot splines but omits variable selection, providing stable, interpretable models. Using biomarker data from large studies, this design identifies subgroups dynamically, allowing early trial termination or enrollment adjustments. Simulations in this specific context show FK improves precision, efficiency, and subgroup detection, offering practical benefits over FK-BMA and advancing precision medicine for OSA.

Nikhil Kumar Rajput, Riya Bansal

Quantum computing holds transformative potential for medical applications, yet efficiently preparing quantum states from complex medical data remains a fundamental challenge. This survey provides a comprehensive examination of current approaches for encoding medical information into quantum systems, analyzing theoretical principles, algorithmic advancements, and practical limitations. It discusses tensor network decomposition, variational quantum algorithms, quantum machine learning techniques, and specialized error mitigation strategies for medical computing. The findings indicate that quantum advantages in medicine rely on leveraging inherent data structures such as spatial correlations in imaging, temporal patterns in physiological signals, and hierarchical biological organization. While current hardware restricts implementations to small-scale problems, emerging methods show potential for near-term use. The study provides a structured framework for assessing when quantum state preparation outperforms classical approaches in medicine, along with implementation guidelines and performance benchmarks.

Gino Martin Valenti

El presente artículo analiza la demanda promovida por una consumidora contra la empresa fabricante de una bebida gaseosa que, al momento de su adquisición, presentaba cuerpos extraños en su interior, habiéndose alterado su contenido. En razón de ello, reclamó los daños y perjuicios sufridos, aun cuando decidió no consumirla. La Cámara de Apelaciones en lo Civil y Comercial de Quinta Nominación de la ciudad de Córdoba, mediante la aplicación del Estatuto del Consumidor, resolvió responsabilizar a la empresa proveedora. Consideró que el producto fue puesto en el mercado en condiciones que implicaban un riesgo para la salud de los consumidores, contrario al deber de seguridad que pesa sobre el proveedor. Es que, en las relaciones de consumo, la protección del derecho a la salud ocupa un rol central, dado que se trata de un derecho fundamental que no admite estándares mínimos, sino una tutela reforzada.

Linlin Wang, Shuang Xie, Aoxue Mei et al.

Abstract Purpose Acute coronary syndromes (ACS) is a leading cause of death worldwide. Albumin and globulin are the main components of serum proteins. The albumin-to-globulin ratio (AGR) is often used to assess nutritional status. However, the clinical significance of the AGR in predicting the prognosis of patients with ACS remains unclear. Patients and methods A total of 1408 patients with ACS who underwent percutaneous coronary intervention (PCI) were consecutively enrolled between January 2016 and December 2018 at The Affiliated Hospital of Chengde Medical University. The follow-up endpoints were defined as cardiac death or recurrent acute myocardial infarction. Results A total of 1363 patients responded in the follow-up period, of whom 49 had MACEs. AGR was significantly different between the MACEs and non-MACE groups. The area under the curve for the AGR was 0.619 (P = 0.004, 95% confidence interval [CI]: 0.542–0.697). The optimal cut-off value for the AGR was determined to be 1.350 using Youden’s index. The cumulative survival rate of the low AGR group was significantly lower than that of the high AGR group, according to the Kaplan–Meier curve (log-rank P = 0.008). Multivariate Cox proportional hazards model showed age ≥ 60 years, HR:2.689 (95%CI:1.288–5.615, P = 0.008), left ventricular ejection fraction (LVEF) < 40%, HR: 3.527, (95%CI: 1.357–9.164, P = 0.010), and AGR < 1.350, HR: 2.180, (95%CI: 1.078–4.407, P = 0.030) were all independent risk factors. A restricted cubic spline showed that a decreasing AGR was correlated with increasing risk of MACEs. Conclusion AGR < 1.350 is an independent prognostic risk factor for patients with ACS undergoing PCI and may be a valuable clinical marker for identifying high-risk patients.

M. Dunnill

Olivia Agatha, Daniela Mutwil-Anderwald, Jhing Yein Tan et al.

Sesquiterpene lactones (STLs) are a prominent group of plant secondary metabolites predominantly found in the Asteraceae family and have multiple ecological roles and medicinal applications. This review describes the ecological significance of STLs, highlighting their roles in plant defense mechanisms against herbivory and as phytotoxins, alongside their function as environmental signaling molecules. We also cover the substantial role of STLs in medicine and their mode of action in health and disease. We discuss the biosynthetic pathways and the various modifications that make STLs one of the most diverse groups of metabolites. Finally, we discuss methods in identifying and predicting STL biosynthesis pathways.