检索结果-内蒙古大学图书馆

Sequential machine learning in prediction of common cancers

informatics in Medicine Unlocked 2022年 30卷

作者： Andjelkovic, Jovan Ljubic, Branimir Hai, Ameen Abdel Stanojevic, Marija Pavlovski, Martin Diaz, Wilson Obradovic, Zoran Temple University Center for Data Analytics and Biomedical Informatics (DABI) Philadelphia 19121 PA United States Rutgers University The Office of Advanced Research Computing (OARC) Piscataway 08854 NJ United States

Cancer is one of the most common causes of death in the world. It is characterized by the multi-stage transformation of normal cells into tumor cells. Early cancer detection can significantly reduce its consequences, which was the objective of many machine learning (ML) published studies. However, most of them focused on microarray, gene expression, or publicly available medical datasets. Almost none offered an approach for predicting cancer through analysis of sequential data, such as Electronic Health Record (EHR) data. This paper presents a sequential ML approach to predict the occurrence of lung cancer, breast cancer, cervical cancer, and liver cell cancer using EHR data. The accuracy of sequence learning models based on long short-term memory (LSTM) and bidirectional gated recurrent units (GRU) were compared to traditional ML methods based on multilayer perceptron, random forest, decision tree, and K-nearest neighbor. The models were trained and tested on 50,606 patient hospitalization histories. Unsupervised and supervised data reduction methods (singular value decomposition (SVD) and a neural network embedding layer) were applied to overcome the challenges of high-dimensionality and sparsity of EHR data. The results provided evidence that for this application GRU outperforms alternatives based on accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), sensitivity (recall), specificity, precision, and F1 score. It was the best performing model with accuracy between 81% (breast cancer) and 88% (liver cancer) on balanced out of sample EHRs. Multilayer perceptron and LSTM manifested comparable performances (accuracies between 78% and 87%) among the alternatives, while decision tree was the worst-performing model. The findings of this study could potentially aid medical professionals in cancer diagnostics, treatment, and prevention. In particular, experiments confirmed that GRU could accurately predict cancer by learning from simplified patient r

关键词： Breast cancer Cervix uteri cancer Liver cell cancer Lung cancer Machine learning

来源：评论

学校读者我要写书评

暂无评论

Using a statewide health information exchange to enhance COVID research and surveillance: The impact of information fragmentation

引用

International Journal of Medical informatics 2025年 203卷

作者： Katie S. Allen Seho Park John Price Xia Ning Titus Schleyer Center for Biomedical Informatics Regenstrief Institute Inc United States Department of Industrial and Data Engineering Hongik University South Korea Biomedical Informatics Computer Science and Engineering Translational Data Analytics Institute The Ohio State University United States School of Medicine Indiana University United States

Objective Use of electronic health data for public health surveillance and research has become almost ubiquitous. However, since many patients routinely receive care from multiple healthcare systems, not all information for a patient may be available in a single system. Thus, using data from only a single healthcare system may unintentionally result in inaccuracies. This study compared the variation in the number of inpatient and emergency department encounters, diagnoses, and laboratory results, as well as COVID prevalence, when data about patients was (1) drawn from a single, major health system; (2) augmented with health information exchange (HIE) data; or (3) retrieved entirely from the HIE. Materials and Methods This study leveraged a registry of 1,343,703 COVID-positive patients covering the state of Indiana containing data from March 6, 2020 − February 14, 2022. The registry included data from the state-wide health information exchange as well as granular data from an individual health system. data elements included encounters, diagnoses, laboratory testing, and death records. Results Enhancing the cohort with data from the HIE increased the average number of encounters per person from 20.25 to 37.05, unique diagnoses from 16.06 to 30.83, and laboratory results from 0.32 to 7.06. The absolute difference between the single-system and HIE cohorts was 22.5% for the estimated prevalence of COVID, 4% for the number of all-cause hospitalizations and -9.4% for emergency department visits. Conclusion Information fragmentation can lead to an incomplete picture, or even errors, in research or clinical settings. This study demonstrates that combining clinical data sources creates variation in results. Understanding the issues presented by information fragmentation, such as bias it may create, is critical to enabling accurate disease surveillance and crisis response planning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Individualized treatment effect was predicted best by modeling baseline risk in interaction with treatment assignment

arXiv

引用

arXiv 2022年

作者： Rekkas, Alexandros Rijnbeek, Peter R. Kent, David M. Steyerberg, Ewout W. Van Klaveren, David Department of Medical Informatics Erasmus Medical Center Rotterdam Netherlands Predictive Analytics and Comparative Effectiveness Center Institute for Clinical Research and Health Policy Studies Tufts Medical Center BostonMA United States Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Public Health Erasmus Medical Center Rotterdam Netherlands

Objective: To compare different risk-based methods for optimal prediction of treatment effects. Study Design and Setting: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk (PI), the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the PI). We predicted absolute benefit using: models with a constant relative treatment effect;stratification in quarters of the PI;models including a linear interaction of treatment with the PI;models including an interaction of treatment with a restricted cubic spline (RCS) transformation of the PI;an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. Results: The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N=4,250;∼785 events). The RCS-model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N=17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. Conclusion: An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions. © 2022, CC BY.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Another estimation of Laplacian spectrum of the Kronecker product of graphs

arXiv

引用

arXiv 2021年

作者： Bašić, Milan Arsić, Branko Obradović, Zoran Department of Computer Science University of Niš Serbia Department of Mathematics and Informatics University of Kragujevac Serbia Department of Computer and Information Sciences Center for Data Analytics and Biomedical Informatics Temple University PhiladelphiaPA United States

The relationships between eigenvalues and eigenvectors of a product graph and those of its factor graphs have been known for the standard products, while characterization of Laplacian eigenvalues and eigenvectors of the Kronecker product of graphs using the Laplacian spectra and eigenvectors of the factors turned out to be quite challenging and has remained an open problem to date. Several approaches for the estimation of Laplacian spectrum of the Kronecker product of graphs have been proposed in recent years. However, it turns out that not all the methods are practical to apply in network science models, particularly in the context of multilayer networks. Here we develop a practical and computationally efficient method to estimate Laplacian spectra of this graph product from spectral properties of their factor graphs which is more stable than the alternatives proposed in the literature. We emphasize that a median of the percentage errors of our estimated Laplacian spectrum almost coincides with the x-axis, unlike the alternatives which have sudden jumps at the beginning followed by a gradual decrease for the percentage errors. The percentage errors confined (confidence of the estimations) up to ±10% for all considered approximations, depending on a graph density. Moreover, we theoretically prove that the percentage errors becomes smaller when the network grows or the edge density level increases. Additionally, some novel theoretical results considering the exact formulas and lower bounds related to the certain correlation coefficients corresponding to the estimated eigenvectors are presented. © 2021, CC0.

关键词： Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Extracting Entities and Topics from News and Criminal Record

arXiv

引用

arXiv 2020年

作者： Pham, Quang Stanojevic, Marija Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Computer and Information Sciences Department Temple University

来源：评论

学校读者我要写书评

暂无评论

Quantitative bias analysis for outcome phenotype error correction in comparative effect estimation: an empirical and synthetic evaluation

arXiv

引用

arXiv 2023年

作者： Weaver, James Ryan, Patrick B. Strauss, Victoria Y. Suchard, Marc A. Swerdel, Joel Prieto-Alhambra, Daniel Centre for Statistics in Medicine Nuffield Department of Orthopedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford United Kingdom Observational Health Data Analytics Janssen Research and Development TitusvilleNJ United States Observational Health Data Sciences and Informatics New YorkNY United States Department of Biomedical Informatics Columbia University Medical Center New YorkNY United States Global Biostatistics & Data Sciences Boehringer Ingelheim Pharma GmbH & Co. KG. Ingelheim Germany Department of Biostatistics Fielding School of Public Health Department of Biomathematics David Geffen School of Medicine UCLA Los AngelesCA United States Medical Informatics Erasmus Medical Centre Rotterdam Netherlands

Outcome phenotype measurement error is rarely corrected in comparative effect estimation studies in observational pharmacoepidemiology. Quantitative bias analysis (QBA) is a misclassification correction method that algebraically adjusts person counts in exposure-outcome contingency tables to reflect the magnitude of misclassification. The extent QBA minimizes bias is unclear because few systematic evaluations have been reported. We empirically evaluated QBA impact on odds ratios (OR) in several comparative effect estimation scenarios. We estimated non-differential and differential phenotype errors with internal validation studies using a probabilistic reference. Further, we synthesized an analytic space defined by outcome incidence, uncorrected ORs, and phenotype errors to identify which combinations produce invalid results indicative of input errors. We evaluated impact with relative bias [(OR-ORQBA)]/OR×100%]. Results were considered invalid if any contingency table cell was corrected to a negative number. Empirical bias correction was greatest in lower incidence scenarios where uncorrected ORs were larger. Similarly, synthetic bias correction was greater in lower incidence settings with larger uncorrected estimates. The invalid proportion of synthetic scenarios increased as uncorrected estimates increased. Results were invalid in common, low incidence scenarios indicating problematic inputs. This demonstrates the importance of accurately and precisely estimating phenotype errors before implementing QBA in comparative effect estimation studies. © 2023, CC BY-NC-ND.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

The ELEVATE-AI LLMs Framework: An Evaluation Framework for Use of Large Language Models in HEOR: An ISPOR Working Group Report

arXiv

引用

arXiv 2024年

作者： Fleurence, Rachael L. Dawoud, Dalia Bian, Jiang Higashi, Mitchell K. Wang, Xiaoyan Xu, Hua Chhatwal, Jagpreet Ayer, Turgay Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States Value Analytics Labs BostonMA United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States

Introduction: Generative Artificial Intelligence, particularly large language models (LLMs), offers transformative potential for Health Economics and Outcomes Research (HEOR). However, evaluating the quality, transparency, and rigor of LLM-assisted research lacks standardized guidance. This article introduces the ELEVATE-AI LLMs framework and checklist, designed to support researchers and reviewers in assessing LLM use in HEOR. Methods: The ELEVATE-AI LLMs framework was developed through a targeted review of existing guidelines and evaluation frameworks. The framework comprises ten evaluation domains, including model characteristics, accuracy, comprehensiveness, and fairness. The accompanying checklist operationalizes the framework. To validate the framework, we applied it to two published studies, demonstrating its usability across different HEOR tasks. Results: The ELEVATE-AI LLMs framework provides a comprehensive structure for evaluating LLM-assisted research, while the checklist facilitates practical application. Validation of the framework and checklist on studies of systematic literature reviews and health economic modeling highlighted their ability to identify strengths and gaps in reporting. Limitations: While the ELEVATE-AI LLMs framework provides robust guidance, its broader generalizability and applicability to diverse HEOR tasks require further empirical testing. Additionally, several metrics adapted from computer science, need further validation in HEOR contexts. Conclusion: The ELEVATE-AI LLMs framework and checklist fill a critical gap in HEOR by offering structured guidance for evaluating LLM-assisted research. By promoting transparency, accuracy, and reproducibility, they aim to standardize and improve the integration of LLMs into HEOR, ensuring their outputs meet the field's rigorous standards. © 2024, CC BY.

关键词： Ability testing

来源：评论

学校读者我要写书评

暂无评论

The spatial zonation of the murine placental vasculature is specified by epigenetic mechanisms

引用

Developmental Cell 2025年第10期60卷 1467-1482.e8页

作者： Gehrs, Stephanie Jakab, Moritz Gutjahr, Ewgenija Gu, Zuguang Weichenhan, Dieter Mallm, Jan-Philipp Mogler, Carolin Schlesner, Matthias Plass, Christoph Schlereth, Katharina Augustin, Hellmut G. Division of Vascular Oncology and Metastasis German Cancer Research Center (DKFZ) Heidelberg 69120 Germany European Center for Angioscience (ECAS) Medical Faculty Mannheim Heidelberg University Mannheim 68167 Germany Faculty of Biosciences Heidelberg University Heidelberg 69120 Germany Institute of Pathology University Clinic Heidelberg Heidelberg 69120 Germany Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Cancer Epigenomics German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and Bioquant Heidelberg 69120 Germany Institute of Pathology TUM School of Medicine Technical University of Munich Munich 80333 Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg 86159 Germany

The labyrinthian fetoplacental capillary network is vital for proper nourishment of the developing embryo. Dysfunction of the maternal-fetal circulation is a primary cause of placental insufficiency. Here, we show that the spatial zonation of the murine placental labyrinth vasculature is controlled by flow-regulated epigenetic mechanisms. Spatiotemporal transcriptomic profiling identified a gradual change in the expression of epigenetic enzymes, including the de novo DNA methyltransferase 3a (DNMT3A). Loss of Dnmt3a resulted in DNA hypomethylation and perturbation of zonated placental gene expression. The resulting global DNA hypomethylation impaired the angiogenic capacity of endothelial cells. Global or endothelium-predominant deletion of Dnmt3a resulted in impaired placental vascularization and fetal growth retardation (FGR). Human placental endothelial gene expression profiling associated preeclampsia with reduced DNMT3A expression. Collectively, our study identified DMNT3A as critical methylome-regulator of placental endothelial gene expression and function with clinical implications for placental dysfunction, as it occurs during preeclampsia or FGR. © 2024 The Authors

关键词： angiogenesis DNA methylation DNMT3A epigenetic regulation growth retardation placental endothelium spatial zonation

来源：评论

学校读者我要写书评

暂无评论

Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications – an ISPOR Working Group Report

arXiv

引用

arXiv 2024年

作者： Fleurence, Rachael L. Wang, Xiaoyan Bian, Jiang Higashi, Mitchell K. Ayer, Turgay Xu, Hua Dawoud, Dalia Chhatwal, Jagpreet Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States Value Analytics Labs BostonMA United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States

Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applications, including systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches such as prompt engineering (zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and the use of domain-specific models are introduced to improve AI accuracy and reliability. Results: Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration. The article discusses strategies to improve the accuracy of these AI tools. Conclusion: Generative AI could transform HEOR by increasing efficiency and accuracy across various applications. However, its full potential can only be realized by building HEOR expertise and addressing the limitations of current AI technologies. As AI evolves, ongoing research and innovation will shape its future role in the field. © 2024, CC BY.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Large Language Models in Mental Health Care: a Scoping Review

arXiv

引用

arXiv 2024年

作者： Hua, Yining Liu, Fenglin Yang, Kailai Li, Zehan Na, Hongbin Sheu, Yi-Han Zhou, Peilin Moran, Lauren V. Ananiadou, Sophia CliDon, David A. Beam, Andrew Torous, John Department of Epidemiology Harvard T.H. Chan School of Public Health MA United States Department of Psychiatry Beth Israel Deaconess Medical Center MA United States Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science The University of Manchester Manchester United Kingdom School of Biomedical Informatics University of Texas Health Science at Houston Houston United States Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Center for Precision Psychiatry Massachusetts General Hospital MA United States Department of Psychiatry Harvard Medical School MA United States Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China Division of Psychotic Disorders McLean Hospital MA United States The Alan Turing Institute London United Kingdom

The integration of large language models (LLMs) in mental health care is an emerging field. There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings. This review aims to provide a comprehensive overview of the use of LLMs in mental health care, assessing their efficacy, challenges, and potential for future applications. A systematic search was conducted across multiple databases including PubMed, Web of Science, Google Scholar, arXiv, medRxiv, and PsyArXiv in November 2023. All forms of original research, peer-reviewed or not, published or disseminated between October 1, 2019, and December 2, 2023, are included without language restrictions if they used LLMs developed after T5 and directly addressed research questions in mental health care settings. From an initial pool of 313 articles, 34 met the inclusion criteria based on their relevance to LLM application in mental health care and the robustness of reported outcomes. Diverse applications of LLMs in mental health care are identified, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. LLMs hold substantial promise for enhancing mental health care. For their full potential to be realized, emphasis must be placed on developing robust datasets, development and evaluation frameworks, ethical guidelines, and interdisciplinary collaborations to address current limitations. © 2024, CC BY.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：