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Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration

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arXiv 2023年

作者： Cimpean, Alexandra Verstraeten, Timothy Willem, Lander Hens, Niel Nowé, Ann Libin, Pieter Artificial Intelligence Lab Department of Computer Science Vrije Universiteit Brussel Brussels Belgium Data Science Institute Interuniversity Institute of Biostatistics and statistical Bioinformatics UHasselt Hasselt Belgium Centre for Health Economics Research and Modelling Infectious Diseases Vaccine & Infectious Disease Institute University of Antwerp Antwerp Belgium University of Antwerp Antwerp Belgium

Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models are computationally intensive, it is pivotal to identify optimal strategies within a reasonable computational budget. Moreover, due to the high societal impact associated with the implementation of preventive strategies, uncertainty regarding decisions should be communicated to policy makers, which is naturally embedded in a Bayesian approach. We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework in combination with a Bayesian anytime m-top exploration algorithm. m-top exploration allows the algorithm to learn m policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty. The anytime component provides policy advisors with flexibility regarding the computation time and the desired confidence, which is important as it is difficult to make this trade-off beforehand. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies that minimize the number of infections and hospitalisations. Through experiments we show that our method can efficiently identify the m-top policies, which is validated in a scenario where the ground truth is available. Finally, we explore how vaccination policies can best be organised under different contact reduction schemes and we investigate the impact of vaccine uptake proportions (i.e., the proportion of individuals that will comply with the strategy and take the vaccine). Through these experiments, we show that the top policies follow a clear trend regarding the prioritised age groups and assigned vaccine type, which provides insights for future vaccination campaigns. Furthermore, our experiments suggest that the uptake proportion has only a l

关键词： COVID-19

Scalable Bayesian inference for the generalized linear mixed model

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arXiv 2024年

作者： Berchuck, Samuel I. Medeiros, Felipe Mukherjee, Sayan Agazzi, Andrea Department of Biostatistics & Bioinformatics Statistical Science Duke University NC27710 United States Department of Ophthalmology University of Miami MiamiFL33136 United States Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig04105 Germany Department of Mathematics Università di Pisa PI Pisa56127 Italy

The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings. The focus of this paper is scalable statistical inference for the GLMM, where we define statistical inference as: (i) estimation of population parameters, and (ii) evaluation of scientific hypotheses in the presence of uncertainty. Artificial intelligence (AI) learning algorithms excel at scalable statistical estimation, but rarely include uncertainty quantification. In contrast, Bayesian inference provides full statistical inference, since uncertainty quantification results automatically from the posterior distribution. Unfortunately, traditional Bayesian inference algorithms, including Markov Chain Monte Carlo (MCMC), become computationally intractable in big data settings. In this paper, we introduce a statistical inference algorithm for GLMM that leverages the scalability of modern AI algorithms with guaranteed uncertainty quantification that accompanies Bayesian inference. Our algorithm is an extension of stochastic gradient MCMC with novel contributions that address the treatment of correlated data (i.e., intractable marginal likelihood) and proper posterior variance estimation. Through theoretical and empirical results, we establish our algorithm’s statistical inference properties, and apply the method in a large electronic health records database. Copyright © 2024, The Authors. All rights reserved.

关键词： Big data

A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation

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arXiv 2024年

作者： Tang, Xiangru Dai, Howard Knight, Elizabeth Wu, Fang Li, Yunyang Li, Tianxiao Gerstein, Mark Department of Computer Science Yale University New HavenCT06520 United States School of Medicine Yale University New HavenCT06520 United States Computer Science Department Stanford University CA94305 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://***/gersteinlab/GenAI4Drug. © 2024, CC BY.

关键词： Proteins

Adapting Segment Anything Model for Ulcer Segmentation in Inflammatory Bowel Disease

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Adapting Segment Anything Model for Ulcer Segmentation in In...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Lingrui Cai Ryan W. Stidham Emily Wittrup Kayvan Najarian Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor Michigan Department of Internal Medicine Division of Gastroenterology Michigan Medicine Ann Arbor Michigan Michigan Institute for Data Science University of Michigan Ann Arbor Michigan Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann Arbor Michigan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Inflammatory Bowel Disease (IBD) is a widespread gastrointestinal disorder characterized by inflammation and ulceration, affecting millions of individuals. Mucosal injury assessment through colonoscopy plays a crucial role in determining disease severity and therapeutic efficacy in IBD. This study draws inspiration from the Segment Anything Model (SAM) framework and tailors it for ulcer segmentation using a Parameter-Efficient Transfer Learning (PETL) approach and incorporating binary cross-entropy loss with boundary loss. Comparative assessments against state-of-the-art UNet-based convolutional neural network models underscore SAM’s superior performance, especially when faced with limited training samples. Our ulcer segmentation models demonstrate Dice, IoU, and 95%HD scores of 61.92±0.69, 75.18±0.38, and 10.07±0.05 in the colon dataset, and 55.60±0.82, 77.00±0.67, and 7.74±0.23 in the ileum dataset, respectively. This study highlights the potential of our approach to significantly enhance the efficiency and accuracy of IBD diagnosis and assessment through cutting-edge computer vision techniques.

关键词： Inflammatory bowel disease Training Adaptation models Accuracy Computational modeling Transfer learning Colonoscopy Transformers Colon Convolutional neural networks

Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health

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arXiv 2024年

作者： Zhou, Songchi Song, Ge Sun, Haoqi Zhang, Deyun Leng, Yue Westover, M. Brandon Hong, Shenda National Institute of Health Data Science Peking University China Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University China Department of Neurology Beth Israel Deaconess Medical Center Harvard Medical School United States HeartVoice Medical Technology Department of Psychiatry and Behavioral Sciences University of California San Francisco United States

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. It provides limited information about the duration of arousal and may hinder research on sleep fragmentation and relevant sleep disorders. To address this issue, we propose a deep learning method for automatic and scalable annotation of continuous sleep depth index (SDI) using existing discrete sleep staging labels. Our approach was validated using polysomnography from over 10,000 recordings across four large-scale cohorts. The results showcased a strong correlation between the decrease in sleep depth index and the increase in duration of arousal. Specific case studies indicated that the sleep depth index captured more nuanced sleep structures than conventional sleep staging. Gaussian mixture models based on the digital biomarkers extracted from the sleep depth index identified two subtypes of sleep, where participants in the disturbed sleep group had a higher prevalence of sleep apnea, insomnia, poor subjective sleep quality, hypertension, and cardiovascular disease. The disturbed subtype was associated with a 42% (hazard ratio 1.42, 95% CI 1.24-1.62) increased risk of mortality and a 29% (hazard ratio 1.29, 95% CI 1.00-1.67) increased risk of fatal cardiovascular disease. Our study underscores the utility of the proposed method for continuous sleep depth annotation, which could reveal more detailed information about the sleep structure and yield novel digital biomarkers for routine clinical use in sleep medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Sleep research

Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

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Impact of CLAHE-based image enhancement for diabetic retinop...

7th International Conference on Computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

The prognosis of clinical stage IIIa non-small cell lung cancer in Taiwan

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Cancer Medicine 2023年第16期12卷 17087-17097页

作者： Cheng, Ya-Fu Huang, Jing-Yang Lin, Ching-Hsiung Wang, Bing-Yen Division of Thoracic Surgery Department of Surgery Changhua Christian Hospital Changhua Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Center for Health Data Science Chung Shan Medical University Hospital Taichung Taiwan Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan Department of Internal Medicine Division of Chest Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan

Lung cancer is the leading cause of cancer death. The treatment of stage IIIa remained the most controversial of all stages of non-small cell lung cancer (NSCLC). We reported on the heterogenicity and current treatment strategies of stage IIIa NSCLC in Taiwan. This study is a retrospective analysis using data from the Taiwan Society of Cancer Registry between January 2010 and December 2018. 4232 patients with stage IIIa NSCLC were included. Based on cell type, the best 5-year OS (40.40%) occurred among adenocarcinoma victims. The heterogenicity of T1N2 had the best 5-year OS (47.62%), followed by T4N0 (39.82%), and the others. Patients who underwent operations had better 5-year OS (over 50%) than those who did not (less than 30%). Segmentectomy (75.28%) and lobectomy (54.06%) showed better 5-year OS than other surgical methods (less than 50%). In multivariable analysis, young age, female, lower Charlson Comorbidity Index score, adenocarcinoma cell type, well differentiated, T1N2/T4N0 heterogenicity, treatment with operation, and segmentectomy/lobectomy/bilobectomy were significant factors. In conclusions, the heterogenicity of T1N2 had the best outcomes followed by T4N0. Patients received surgical treatment revealed much better outcomes than those did not. As always, multimodal therapies with individualized treatment tend to provide better survival outcomes. © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

关键词： heterogenicity non-small cell lung cancer outcomes stage IIIa Taiwan

Multiple Imputation Method for High-Dimensional Neuroimaging data

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arXiv 2023年

作者： Lu, Tong Chen, Chixiang Huang, Hsin-Hsiung Kochunov, Peter Hong, Elliot Chen, Shuo Department of Mathematics University of Maryland College Park United States Division of Biostatistics and Bioinformatics School of Medicine University of Maryland United States Department of Statistics and Data Science University of Central Florida United States Department of Psychiatry and Behavioral Science University of Texas Health Science Center United States Maryland Psychiatric Research Center School of Medicine University of Maryland United States

Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is therefore crucial to conduct appropriate statistical methods to address this issue. While multiple imputation is a popular technique for handling missing data, its application to neuroimaging data is hindered by high dimensionality and complex dependence structures of multivariate neuroimaging variables. To tackle this challenge, we propose a novel approach, named High dimensional Multiple Imputation (HIMA), based on Bayesian models. HIMA develops a new computational strategy for sampling large covariance matrices based on a robustly estimated posterior mode, which drastically enhances computational efficiency and numerical stability. To assess the effectiveness of HIMA, we conducted extensive simulation studies and real-data analysis using neuroimaging data from a Schizophrenia study. HIMA showcases a computational efficiency improvement of over 2000 times when compared to traditional approaches, while also producing imputed datasets with improved precision and stability. © 2023, CC BY.

关键词： Covariance matrix