Health data science最新文献

Background: Magnetic resonance imaging (MRI) is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics. Acquiring high-quality MRI data is of paramount importance. Super-resolution reconstruction serves as a post-acquisition method capable of improving MRI data quality. Current methods predominantly utilize convolutional neural networks in super-resolution reconstruction. However, convolutional layers have inherent limitations in capturing extensive spatial dependencies due to their localized nature. Methods: We developed a new methodology that enables rapid and high-quality MRI data acquisition through a novel super-resolution approach. We proposed an innovative architecture using transformers to exploit long-range spatial dependencies present in images, allowing for an unsupervised learning framework specifically designed for super-resolution tasks tailored to individual subject. We validated our approach using both simulated data and clinical data comprising 40 scans acquired with a 3-T MRI system. Results: We obtained images with T2 contrast at an isotropic spatial resolution of 500 μm in just 4 min of imaging time, and simultaneously, the signal-to-noise ratio and contrast-to-noise ratio were improved by 13.23% and 18.45%, respectively, in comparison to current leading super-resolution techniques. Conclusions: The results demonstrated that incorporating long-range spatial dependencies substantially improved super-resolution reconstruction, thereby allowing for the acquisition of high-quality MRI data with reduced imaging time.

Background: Clinical and biomedical research in low-resource settings often faces substantial challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts. Transfer learning (TL), a machine learning technique, emerges as a powerful solution by utilizing knowledge from pretrained models to enhance the performance of new models, offering promise across various healthcare domains. Despite its conceptual origins in the 1990s, the application of TL in medical research has remained limited, especially beyond image analysis. This review aims to analyze TL applications, highlight overlooked techniques, and suggest improvements for future healthcare research. Methods: Following the PRISMA-ScR guidelines, we conducted a search for published articles that employed TL with structured clinical or biomedical data by searching the SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL databases. Results: We screened 5,080 papers, with 86 meeting the inclusion criteria. Among these, only 2% (2 of 86) utilized external studies, and 5% (4 of 86) addressed scenarios involving multi-site collaborations with privacy constraints. Conclusions: To achieve actionable TL with structured medical data while addressing regional disparities, inequality, and privacy constraints in healthcare research, we advocate for the careful identification of appropriate source data and models, the selection of suitable TL frameworks, and the validation of TL models with proper baselines.

Background: The traditional manual literature screening approach is limited by its time-consuming nature and high labor costs. A pressing issue is how to leverage large language models to enhance the efficiency and quality of evidence-based evaluations of drug efficacy and safety. Methods: This study utilized a manually curated reference literature database-comprising vaccine, hypoglycemic agent, and antidepressant evaluation studies-previously developed by our team through conventional systematic review methods. This validated database served as the gold standard for the development and optimization of LitAutoScreener. Following the PICOS (Population, Intervention, Comparison, Outcomes, Study Design) principles, a chain-of-thought reasoning approach with few-shot learning prompts was implemented to develop the screening algorithm. We subsequently evaluated the performance of LitAutoScreener using 2 independent validation cohorts, assessing both classification accuracy and processing efficiency. Results: For respiratory syncytial virus vaccine safety validation title-abstract screening, our tools based on GPT (GPT-4o), Kimi (moonshot-v1-128k), and DeepSeek (deepseek-chat 2.5) demonstrated high accuracy in inclusion/exclusion decisions (99.38%, 98.94%, and 98.85%, respectively). Recall rates were 100.00%, 99.13%, and 98.26%, with statistically significant performance differences (χ ² = 5.99, P = 0.048), where GPT outperformed the other models. Exclusion reason concordance rates were 98.85%, 94.79%, and 96.47% (χ ² = 30.22, P < 0.001). In full-text screening, all models maintained perfect recall (100.00%), with accuracies of 100.00% (GPT), 100.00% (Kimi), and 99.45% (DeepSeek). Processing times averaged 1 to 5 s per article for title-abstract screening and 60 s for full-text processing (including PDF preprocessing). Conclusions: LitAutoScreener offers a new approach for efficient literature screening in drug intervention studies, achieving high accuracy and significantly improving screening efficiency.

Background: Recently, several cutting-edge experimental studies have directed chimeric antigen receptor (CAR)-T therapies toward specific renal diseases, revealing substantial renal benefits. Prior to widespread implementation of these animal experiments and potentially clinical trials, it is crucial to assess the renal safety of CAR-T therapies using real-world safety evidence. Methods: Our focus was on utilizing 4 algorithms, including disproportionality analysis, based on the US Food and Drug Administration Adverse Event Reporting System database, to filter positive signals of acute and chronic renal injury associated with 6 CAR-T therapies. Further determination of causality was achieved through Mendelian randomization (MR) for drugs associated with renal injury events showing a correlation. Results: Six therapies were evaluated involving a total of 9,770 patients, with only acute kidney injury (AKI) identified as associated with idecabtagene vicleucel treatment using 4 algorithmic thresholds, including disproportionality analysis. Subsequently, MR revealed no causal relationship between the idecabtagene vicleucel target B cell maturation antigen and the risk of AKI (P = 0.576), a finding validated in another independent dataset (P = 0.734). Conclusion: CAR-T therapies do not directly cause renal damage and necessitate controlling adverse renal risks during or after treatment, such as cytokine release syndrome. Future research efforts should rigorously optimize these aspects to better cater to nephrologists' requirements.

Background: Major depressive disorder (MDD) and autism spectrum disorder (ASD) are complex and heterogeneous neuropsychiatric disorders with overlapping symptoms, presenting remarkable challenges for accurate diagnosis. Leveraging functional neuroimaging data offers an opportunity to develop more robust, data-driven approach for psychiatric disorder detection. However, existing methods often struggle to capture the long-term dependencies and dynamic patterns inherent in such data, particularly across diverse imaging sites. Methods: We propose Multiscale Contextual Mamba (MSC-Mamba), a Mamba-based model designed for capturing long-term dependencies in multivariate time-series data while maintaining linear scalability, allowing us to account for long-range interactions and subtle dynamic patterns within the brain's functional networks. One of the main advantages of MSC-Mamba is its ability to leverage the distinct characteristics of time-series data, allowing it to generate meaningful contextual information across various scales. This method effectively addresses both channel-mixing and channel-independence scenarios, facilitating the selection of relevant features for prediction by considering both global and local contexts at multiple scales. Results: Two large-scale multisite functional magnetic resonance imaging datasets, including REST-meta-MDD (n = 1,642) and Autism Brain Imaging Data Exchange (ABIDE) (n = 1,022), were used to validate the performance of our proposed approach. MSC-Mamba has achieved state-of-the-art performance, with an accuracy of 69.91% for MDD detection and 73.08% for ASD detection. The results demonstrate the model's robust generalization across imaging sites and its sensitivity to intricate brain network dynamics. Conclusions: This paper demonstrates the potential of state-space models in advancing psychiatric neuroimaging research. The findings suggest that such models can significantly enhance detection accuracy for MDD and ASD, pointing toward more reliable, data-driven diagnostic tools in psychiatric disorder detection.

Background: Alterations of brain connectivity within resting-state networks (RSNs) have been widely reported in observational studies on epilepsy. However, the causal relationship between epilepsy and structural connectivity (SC)/functional connectivity (FC) within RSNs remain unclear. We conducted a bidirectional two-sample Mendelian randomization (MR) to explore the causal relationship between epilepsy subtypes and brain connectivity properties within RSNs. Methods: Genetic instruments were obtained from the latest genome-wide association studies (GWAS) of 69,995 individuals (N _cases = 27,559, N _controls = 42,436) issued by the International League Against Epilepsy. The GWAS summary SC/FC data within RSNs (N _SC = 23,985, N _FC = 24,336) were sourced from the Center for Neurogenomics and Cognitive Research. We investigate the causal relationship between epilepsy subtypes and brain connectivity within RSNs through a bidirectional two-sample MR analysis. Results: We found that the increased risk of generalized genetic epilepsy is consistent with a causal effect on dorsal attention and somatomotor FC. In the reverse MR analysis, there was no suggestive causal effect of FC/SC connectivity on epilepsy subtypes. Conclusions: This study shed light on the associations of FC/SC levels within the RSNs and epilepsy along with its subtypes. This insight could yield crucial intervention strategies to different subtypes of epilepsy at the level of brain structure and functional networks.

Importance: Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders. Self-supervised learning (SSL) models offer a transformative approach for mapping dependencies in functional neuroimaging data. Leveraging the intrinsic organization of brain signals for comprehensive feature extraction, these models enable the analysis of critical neurofunctional features within a clinically relevant framework, overcoming challenges related to data heterogeneity and the scarcity of labeled data. Highlight: This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data, such as functional magnetic resonance imaging and electroencephalography, with a specific focus on their applications in various neuropsychiatric disorders. We discuss 3 main categories of SSL methods: contrastive learning, generative learning, and generative-contrastive methods, outlining their basic principles and representative methods. Critically, we highlight the potential of SSL in addressing data scarcity, multimodal integration, and dynamic network modeling for disease detection and prediction. We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer's disease, Parkinson's disease, and epilepsy, demonstrating their potential in downstream neuropsychological applications. Conclusion: SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders. Despite current limitations in interpretability and data heterogeneity, the potential of SSL for future clinical applications, particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings, is substantial.

Background: Advances in artificial intelligence have enabled the simulation of human-like behaviors, raising the possibility of using large language models (LLMs) to generate synthetic population samples for research purposes, which may be particularly useful in health and social sciences. Methods: This paper explores the potential of LLMs to simulate population samples mirroring real ones, as well as the feasibility of using personality questionnaires to assess the personality of LLMs. To advance in that direction, 2 experiments were conducted with GPT-4o using the Eysenck Personality Questionnaire Revised-Abbreviated (EPQR-A) in 6 languages: Spanish, English, Slovak, Hebrew, Portuguese, and Turkish. Results: We find that GPT-4o exhibits distinct personality traits, which vary based on parameter settings and the language of the questionnaire. While the model shows promising trends in reflecting certain personality traits and differences across gender and academic fields, discrepancies between the synthetic populations' responses and those from real populations remain. Conclusions: These inconsistencies suggest that creating fully reliable synthetic population samples for questionnaire testing is still an open challenge. Further research is required to better align synthetic and real population behaviors.

Background: The use of antidepressants in the treatment of bipolar depression remains controversial due to concerns about their potential to induce mood polarity switches. This multinational observational study aims to examine the association between the use of antidepressants and the risk of hypomanic/manic switch among bipolar depressive patients. Methods: Four electronic health record databases (IQVIA Disease Analyzer Germany, IQVIA Disease Analyzer France, IQVIA US Hospital Charge Data Master, and Beijing Anding Hospital) and one administrative claims database (IQVIA US Open Claims) were analyzed, and the study period covered from January 2013 until December 2017. Treatment patterns of patients with bipolar depression were collected. The hazard ratio (HR) was calculated by comparing the incidence of hypomanic/manic switch in patients who received antidepressants (AD group) with that in those who did not receive any antidepressant (non-AD group) in 730 days after the date of the first diagnosis of bipolar depression. Results: The analysis included a total of 122,843 patients from the 5 databases; 60.6% of them received antidepressants for bipolar depression. Across the 5 data sources, the mean age at index date ranged from 37.50 (15.72) to 52.10 (16.22) years. After controlling potential confounders by propensity score matching, the AD group's manic switch risk was not significantly higher than the non-AD group's (HR 1.04 [95% CI, 0.96 to 1.13]; P = 0.989). Additionally, no statistically significant difference was observed between patients prescribed antimanic drugs and those who were not (HR 0.69 [95% CI, 0.38 to 1.25]; P = 0.535). Conclusions: This study indicated that antidepressants were widely used in clinical settings for managing bipolar depression. The use of antidepressants was not associated with the risk of mania/hypomania switch when compared to non-antidepressants treatment. Therefore, antidepressants could be considered a treatment option for bipolar depression.