Medical long-tailed learning for imbalanced data: Bibliometric analysis

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors

BACKGROUND

Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affecting birth weight (BW).

OBJECTIVE

This study introduces a robust deep neural network for a clinical decision-support system designed to early predict neonatal BW, using data available during early pregnancy, with enhanced precision. This innovative system incorporates a comprehensive array of maternal factors, placing particular emphasis on nutritional elements alongside physiological and lifestyle variables.

METHODS

We employed and validated various traditional machine learning models as well as an interpretable deep learning model using the TabNet architecture, noted for its proficient handling of tabular data and high level of interpretability. The efficacy of these models was evaluated against extensive datasets that encompass a broad spectrum of maternal health indicators.

RESULTS

The TabNet model exhibited outstanding predictive capabilities, achieving an accuracy of 96% and an area under the curve (AUC) of 0.96. Significantly, maternal vitamin B12 and folate status emerged as pivotal predictors of BW, emphasizing the crucial role of nutritional factors in influencing neonatal health outcomes.

CONCLUSIONS

Our results demonstrate the substantial benefits of integrating multimodal maternal factors into predictive models for neonatal BW, markedly enhancing the precision over traditional AI methods. The developed decision-support system not only has a possible application in prenatal care but also provides actionable insights that can be leveraged to mitigate the risks associated with LBW, thereby improving clinical decision-making processes and outcomes.

An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning

Purpose

To develop an intelligent grading model for myopic maculopathy based on a long-tail learning framework, using the improved loss function LTBSoftmax. The model addresses the long-tail distribution problem in myopic maculopathy data to provide preliminary grading, aiming to improve grading capability and efficiency.

Methods

This study includes a data set of 7529 color fundus photographs. Experienced ophthalmologists meticulously annotated the ground truth. A new intelligent grading model for myopic maculopathy was constructed using the improved loss function LTBSoftmax, which predicts lesions by locally enhancing feature extraction with ND Block. Standard grading metrics were selected to evaluate the LTBSoftmax model.

Results

The improved model demonstrated excellent performance in diagnosing four types of myopic maculopathy, achieving a κ coefficient of 88.89%. Furthermore, the model's size is 18.7 MB, which is relatively smaller compared to traditional models, indicating that the model not only achieves a high level of agreement with expert diagnoses but is also more efficient in terms of both storage and computational resources. These metrics further validate the model's well-conceived design and superiority in practical applications.

Conclusions

The intelligent grading system, using long-tailed learning strategies, effectively improves the classification of myopic maculopathy, offering a practical grading tool for clinicians, particularly in areas with limited resources.

Translational Relevance

This model translates long-tail learning research into a practical grading tool for myopic maculopathy. It addresses data imbalance with the improved LTBSoftmax loss function, achieving high accuracy and efficiency. By enhancing feature extraction with ND Block, it provides reliable grading support for clinicians, especially in resource-limited settings.

The application of artificial intelligence in stroke research: A bibliometric analysis

Background

Currently, artificial intelligence (AI) has been widely used for the prediction, diagnosis, evaluation and rehabilitation of stroke. However, the quantitative and qualitative description of this field is still lacking.

Objective

This study aimed to summarize and elucidate the research status and changes in hotspots on the application of AI in stroke over the past 20 years through bibliometric analysis.

Materials and Methods

Publications on the application of AI in stroke in the past two decades were retrieved from the Web of Science Core Collection. Microsoft Excel was used to analyze the annual publication volume. The cooperation network map among countries/regions was generated on an online platform (https://bibliometric.com/). CiteSpace was used to visualize the co-occurrence of institutions and analyze the timeline view of references and burst keywords. The network visualization map of keywords co-occurrence was generated by VOSviewer.

Results

A total of 4437 publications were included. The annual number of published documents shows an upwards trend. The USA published the most documents and has the top 3 most productive institutions. Journal of Neuroengineering and Rehabilitation and Stroke are the journals with the most publications and citations, respectively. The keywords co-occurrence network classified the keywords into four themes, that is "rehabilitation," "machine learning," "recovery" and "upper limb function." The top 3 keywords with the strongest burst strength were "arm," "upper limb" and "therapy." The most recent keywords that burst after 2020 and last until 2023 included "scores," "machine learning," "natural language processing" and "atrial fibrillation."

Conclusion

The USA shows a leading position in this field. At present and in the next few years, research in this field may focus on the prediction/rapid diagnosis of potential stroke patients by using machine learning, deep learning and natural language processing.

Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics

This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that integrates bibliometric analysis, network analysis, thematic mapping, and latent Dirichlet allocation, the study explores the terrain and prospective developments within this usage based on data analytics. Through a meticulous examination of the analyzed articles, the present study identifies seven key thematic areas, highlighting the diverse applications and interdisciplinary nature of blockchain in clinical trials. Our findings reveal blockchain capability to enhance data management, participant consent processes, as well as overall trial transparency, efficiency, and security. Additionally, the investigation discloses the emerging synergy between blockchain and advanced technologies, such as artificial intelligence and federated learning, proposing innovative directions for improving clinical research methodologies. Our study underscores the collaborative efforts in dealing with the complexities of integrating blockchain into the areas of clinical trials and healthcare, delineating the transformative potential of blockchain technology in revolutionizing these areas by addressing challenges and promoting practices of efficient, secure, and transparent research. The delineated themes and networks of collaboration provide a blueprint for future inquiry, showing the importance of empirical research to narrow the gap between theoretical promise and practical implementation.

The Potential Impact of the Gut Microbiota on Neonatal Brain Development and Adverse Health Outcomes

Over the past decade, microbiome research has significantly expanded in both scope and volume, leading to the development of new models and treatments targeting the gut-brain axis to mitigate the effects of various disorders. Related research suggests that interventions during the critical period from birth to three years old may yield the greatest benefits. Investigating the substantial link between the gut and brain during this crucial developmental phase raises fundamental issues about the role of microorganisms in human health and brain development. This underscores the importance of focusing on the prevention rather than the treatment of neurodevelopmental and neuropsychiatric disorders. The present review examines the gut microbiota from birth to age 3, with a particular focus on its potential relationship with neurodevelopment. This review emphasizes the immunological mechanisms underlying this relationship. Additionally, the study investigates the impact of the microbiome on cognitive development and neurobehavioral issues such as anxiety and autism. Importantly, it highlights the need to integrate mechanistic studies of animal models with epidemiological research across diverse cultures to better understand the role of a healthy microbiome in early life and the implications of dysbiosis. Furthermore, this review summarizes factors contributing to the transmission of gut microbiome-targeted therapies and their effects on neurodevelopment. Recent studies on environmental toxins known to impact neurodevelopment are also reviewed, exploring whether the microbiota may mitigate or modulate these effects.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.