Search Results (52)

(1) Background: Data literacy is becoming increasingly important for healthcare professionals in both outpatient care and research. Since healthcare data and the possibilities for its use and misuse are increasing in these areas, healthcare professionals need diverse knowledge regarding the collection, use and evaluation of data. A core component of this is an understanding of the ethical, legal, and social implications (ELSI) of working with health data. (2) Methods: Within the DIM.RUHR project (Data Competence Center for Interprofessional use of Health Data in the Ruhr Metropolis), the challenge of training in data literacy for different healthcare professionals is addressed. Based on a learning objectives matrix for interprofessional data literacy education, an ELSI concept was developed through collaboration with interprofessional project partners. The study was conducted between December 2024 and April 2025. (3) Results: The foundational structure of the ELSI concept was based on the learning objectives matrix and an unstructured literacy search for ELSI concepts in similar contexts. Using an iterative design-based research approach, a group of experts from different fields (didactics, applied ethics, health sciences, law, sociology, informatics, and psychology) developed an ELSI concept for healthcare professionals. The following categories were identified as crucial: 1. philosophy of science: a basic understanding of science and the hurdles and opportunities; 2. ethics: an overview of the biomedical principles and a technological assessment; 3. law: an overview of the reservation of permission and self-determination; 4. social aspects: an overview of health inequalities and different forms of power relations and imbalances. (4) Conclusions: The ELSI concept can be used in the orientation of healthcare professionals in outpatient care and research—regardless of their profession—to develop data competencies, with the aim of providing a holistic view of the challenges and potential in the collection, use, and evaluation of healthcare data. The DIM.RUHR project’s approach is to develop open educational resources that build on the ELSI concept to teach specific skills at different competence levels. Full article

Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biomedical and Health Informatics 2017 (ICBHI2017) database stands as one of the most authoritative open-access RSA datasets. This review systematically examines 135 technical publications utilizing the database, and a comprehensive and timely summary of RSA methodologies is offered for researchers and practitioners in this field. Specifically, this review covers signal processing techniques including data resampling, augmentation, normalization, and filtering; feature extraction approaches spanning time-domain, frequency-domain, joint time–frequency analysis, and deep feature representation from pre-trained models; and classification methods for adventitious sound (AS) categorization and pathological state (PS) recognition. Current achievements for AS and PS classification are summarized across studies using official and custom data splits. Despite promising technique advancements, several challenges remain unresolved. These include a severe class imbalance in the dataset, limited exploration of advanced data augmentation techniques and foundation models, a lack of model interpretability, and insufficient generalization studies across clinical settings. Future directions involve multi-modal data fusion, the development of standardized processing workflows, interpretable artificial intelligence, and integration with broader clinical data sources to enhance diagnostic performance and clinical applicability. Full article

Background: Late-onset pre-eclampsia (LO-PE) remains difficult to predict because placental angiogenic markers perform poorly once maternal cardiometabolic factors dominate. Methods: We reanalyzed a publicly available cell-free RNA (cfRNA) cohort (12 EO-PE, 12 LO-PE, and 24 matched controls). After RNA-seq normalization, we derived LO-PE candidate genes using (i) differential expression and (ii) elastic-net feature selection. Predictive accuracy was assessed with nested Monte-Carlo cross-validation (10 × 70/30 outer splits; 5-fold inner grid-search for λ). Results: The best LO-PE elastic-net model achieved a mean ± SD AUROC of 0.88 ± 0.08 and F1 of 0.73 ± 0.17—substantially higher than an EO-derived baseline applied to the same samples (AUROC ≈ 0.69). Enrichment analysis highlighted immune-tolerance and metabolic pathways; three genes (HLA-G, IL17RB, and KLRC4) recurred across >50% of cross-validation repeats. Conclusions: Plasma cfRNA signatures can outperform existing EO-based screens for LO-PE and nominate biologically plausible markers of immune and metabolic dysregulation. Because the present dataset is small (n = 48) and underpowered for single-gene claims, external validation in larger, multicenter cohorts is essential before clinical translation. Full article

Background: Reusable, machine-processable clinical decision support system (CDSS) rules have not been widely achieved in the medical informatics field. This study introduces the process, results, challenges faced, and lessons learned while converting the United States of America Centers for Disease Control and Prevention (CDC)-recommended immunization schedules (2022) to machine-processable CDSS rules. Methods: We converted the vaccination schedules into tabular, charts, MS Excel, and clinical quality language (CQL) formats. The CQL format can be automatically converted to a machine-processable format using existing tools. Therefore, it was regarded as a machine-processable format. The results were reviewed, verified, and tested. Results: We have developed 465 rules for 19 vaccines in 13 categories, and we have shared the rules via GitHub to make them publicly available. We used cross-review and cross-checking to validate the CDSS rules in tabular and chart formats. The CQL files were tested for syntax and logic with hypothetical patient HL7 FHIR resources. Our rules can be reused and shared by the health IT industry, CDSS developers, medical informatics educators, or clinical care institutions. The unique contributions of our work are twofold: (1) we created ontology-based, machine-processable, and reusable immunization recommendation rules, and (2) we created and shared multiple formats of immunization recommendation rules publicly which can be a valuable resource for medical and medical informatics communities. Conclusions: These CDSS rules can be important contributions to informatics communities, reducing redundant efforts, which is particularly significant in resource-limited settings. Despite the maturity and concise presentation of the CDC recommendations, careful attention and multiple layers of verification and review are necessary to ensure accurate conversion. The publicly shared CDSS rules can also be used for health and biomedical informatics education and training purposes. Full article

Continuous exposure to stress contributes to species diversity and drives microevolutionary processes. It is still unclear, however, whether epigenetic changes, in the form of epimutations such as, for example, differential DNA methylation, are the pre-requisite to speciation events. We hypothesized that continuous stress exposure would increase epigenetic diversity to a higher extent than genetic diversity. In this work, we have analyzed the effect of 25 consecutive generations of UV-C-stress exposure on the Arabidopsis thaliana genome and epigenome. We found no evidence of increased tolerance to UV-C in the progeny of UV-C-stressed plants (F25UV) as compared to the progeny of control plants (F25C). Genetic analysis showed an increased number of single nucleotide polymorphisms (SNPs) and deletions in F25UV plants. Most common SNPs were mutations in cytosines, C to T, C to A, and C to G. Analysis of cytosine methylation showed a significant increase in the percentage of methylated cytosines at CG context in F25UV as compared to F25C or F2C (parental control). The most significant differences between F25UV and either control group were observed in CHG and CHH contexts; the number of hypomethylated cytosines at CHH contexts was over 10 times higher in the F25UC group. F25UV plants clustered separately from other groups in both genomic and epigenomic analyses. GO term analysis of differentially methylated genes revealed enrichments in “DNA or RNA metabolism”, “response to stress”, “response to biotic and abiotic stimulus”, and “signal transduction”. Our work thus demonstrates that continuous exposure to UV-C increases genomic and epigenomic diversity in the progeny, with epigenetic changes occurring in many stress-responsive pathways. Full article

Pathogen infection in animals and plants is recognized in a relatively similar manner by the interaction of pattern recognition receptors on the host cell surface with pathogen-associated molecular patterns on the pathogen surface. Previous work demonstrates that animal pathogenic bacteria can be recognized by plant receptors and alter transcriptome. In this work, we have hypothesized that exposure to human parasites, Cryptosporidium parvum and Giardia lamblia, would also trigger pathogen response in plants, leading to changes in transcriptome. Detached Arabidopsis leaves were exposed for one hour to heat-inactivated Cryptosporidia or Giardia. The transcriptome profile showed large changes in gene expression with significant overlap between two parasites, including upregulated GO terms “cellular response to chitin”, “response to wounding”, “response to oomycetes”, “defense response to fungus”, “incompatible interaction”, and “activation of innate immune response”, and downregulated GO terms “positive regulation of development”, “cell surface”, “regulation of organ growth”, “wax biosynthetic process”, “leaf and shoot morphogenesis”. Uniquely downregulated GO terms in response to Cryptosporidia were GO terms related to chromatin remodelling, something that was not reported before. To conclude, it appears that while Cryptosporidia or Giardia are not pathogens of Arabidopsis, this plant possesses various mechanisms of recognition of pathogenic components of parasites. Full article

This study assessed the association of food security with potential cardio-metabolic risk factors among persons with metabolic syndrome (MetS). Data were derived from the baseline data of a randomized controlled lifestyle intervention trial for individuals with MetS. Household food security, fruit and vegetable intake, perceived food environment, and perceived stress were collected using validated questionnaires. Cardio-metabolic measures assessed with standardized procedures included body mass index, waist circumference, blood pressure, glucose, HbA1c, and lipids. Regression models adjusted for demographics, medication use, and perceived stress were performed. Of a total of 664 participants (median age 56), the majority were female, non-Hispanic White, college-educated, and employed. Food insecurity affected 23% (n = 152), with 5% (n = 31) experiencing very low food security. Food-insecure individuals had significantly higher stress (p < 0.001), lacked healthy food access (p < 0.001), were and less likely to consume ≥2 servings of vegetables/day (p = 0.003). HbA1c was the only cardio-metabolic measure significantly associated with food security (p = 0.007). The link between food insecurity and elevated HbA1c levels highlights the importance of addressing food insecurity and stress to improve metabolic health outcomes in the MetS population. Full article

Pediatric lead poisoning remains a persistent public health problem. Children in the US spend the preponderance of their time at home; thus, housing is an important social determinant of health. Improving health outcomes derived from housing-based sources involves differentiating the risks posed by the existing housing stock. In this paper, we developed a parcel-level lead risk index (LRI) based on external housing conditions and the year of home construction. The purpose of this study was to introduce a housing-based lead risk index (LRI), developed using retrospective data, to estimate parcel-by-parcel variation in housing-based lead risk. We described how the LRI is constructed, relate it to the likelihood of a pediatric occupant’s blood lead level (BLL) > 3.5 µg/dL using Lasso regression (n = 6589), visualized this relationship graphically, and mapped the outcome. We found that mapping the LRI provided more information at a more precise geographic level than was possible using other public health surveillance methods. Full article

Background: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. Methods: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. Results: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. Conclusions: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development. Full article

Background/Objective: To address the opioid epidemic in Kansas City, Missouri, local health systems sought to implement a referral to peer recovery coaches (PRCs) for clients presenting with opioid use disorder. Client referrals were made primarily through health system emergency departments, where PRCs met clients to facilitate linkages to recovery support for up to twelve months. This study aimed to evaluate and improve program implementation with process mapping at three local health systems. Methods: Using a five-phase conceptual framework and three development and implementation domains, providers, administrators, and PRCs were interviewed to identify the process for recognizing clients with opioid use disorders and referring them to PRCs. Serial meetings were held to validate the process maps at three health systems and a distillation of key processes was created to guide future analyses and implementation efforts. Results: A detailed process map for each health system was developed, from which a high-level process map was created to support future implementation efforts. Health system-specific process maps varied, although conceptually coherent elements were identified across each system to diagram a recovery ecosystem to support client referrals to PRCs. Conclusions: By systematically assessing the implementation of the same program across different health systems, critical steps, along with their barriers and facilitators, were identified that can be used to understand the processes of care associated with outcomes and to guide future implementation efforts. Full article

This paper presents a recent investigation into the electromechanical behavior of thermally reduced graphene oxide (rGO) as a strain sensor undergoing repeated large mechanical strains up to 20.72%, with electrical signal output measurement in multiple directions relative to the applied strain. Strain is one the most basic and most common stimuli sensed. rGO can be synthesized from abundant materials, can survive exposure to large strains (up to 20.72%), can be synthesized directly on structures with relative ease, and provides high sensitivity, with gauge factors up to 200 regularly reported. In this investigation, a suspension of graphene oxide flakes was deposited onto Polydimethylsiloxane (PDMS) substrates and thermally reduced to create macroscopic rGO-strain sensors. Electrical resistance parallel to the direction of applied tension ($\widehat{x}$) demonstrated linear behavior (similar to the piezoresistive behavior of solid materials under strain) up to strains around 7.5%, beyond which nonlinear resistive behavior (similar to percolative electrical behavior) was observed. Cyclic tensile testing results suggested that some residual micro-cracks remained in place after relaxation from the first cycle of tensile loading. A linear fit across the range of strains investigated produced a gauge factor of 91.50(Ω/Ω)/(m/m), though it was observed that the behavior at high strains was clearly nonlinear. Hysteresis testing showed high consistency in the electromechanical response of the sensor between loading and unloading within cycles as well as increased consistency in the pattern of the response between different cycles starting from cycle 2. Full article

The integration of artificial intelligence (AI) into healthcare practice has improved patient management and care. Many clinical laboratory specialties have already integrated AI in diagnostic specialties such as radiology and pathology, where it can assist in image analysis, diagnosis, and clinical reporting. As AI technologies continue to advance, it is crucial for biomedical science students to receive comprehensive education and training in AI concepts and applications and to understand the ethical consequences for such development. This review focus on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers. Improving the curriculum can be achieved by introducing specific subjects related to AI such as informatics, data sciences, and digital health. However, there are many challenges to enhancing the curriculum with AI. In this narrative review, we discuss these challenges and suggest mitigation strategies. Full article

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward. Full article

Lower back pain, a common issue among pregnant women, often complicates daily activities like standing up from a chair. Therefore, research into the standing motion of pregnant women is important, and many research studies have already been conducted. However, many of these studies were conducted in highly controlled environments, overlooking everyday scenarios such as using a desk for support when standing up, and their effects have not been adequately tested. To address this gap, we measured multimodal signals for a sit-to-stand (STS) movement with hand assistance and verified the changes using a t-test. To avoid imposing strain on pregnant women, we used 10 non-diseased young adults who wore jackets designed to simulate pregnancy conditions, thus allowing for more comprehensive and rigorous experimentation. We attached surface electromyography (sEMG) sensors to the erector spinae muscles of participants and measured changes in muscle activity, skeletal positioning, and center of pressure both before and after wearing a Maternity-Simulation Jacket. Our analysis showed that the jacket successfully mimicked key aspects of the movement patterns typical in pregnant women. These results highlight the possibility of developing practical strategies that more accurately mirror the real-life scenarios met by pregnant women, enriching the current research on their STS movement. Full article

It is difficult to learn meaningful representations of time-series data since they are sparsely labeled and unpredictable. Hence, we propose bootstrap inter–intra modality at once (BIMO), an unsupervised representation learning method based on time series. Unlike previous works, the proposed BIMO method learns both inter-sample and intra-temporal modality representations simultaneously without negative pairs. BIMO comprises a main network and two auxiliary networks, namely inter-auxiliary and intra-auxiliary networks. The main network is trained to learn inter–intra modality representations sequentially by regulating the use of each auxiliary network dynamically. Thus, BIMO thoroughly learns inter–intra modality representations simultaneously. The experimental results demonstrate that the proposed BIMO method outperforms the state-of-the-art unsupervised methods and achieves comparable performance to existing supervised methods. Full article