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Germline genetics, disease, and exposure to medication influence longitudinal dynamics of clonal hematopoiesis

Haematologica 2025年第4期110卷 1010-1018页

作者： Taralynn Mack Yash Pershad Caitlyn Vlasschaert Cosmin A Bejan Jonathan Brett Heimlich Yajing Li Nicole A Mickels Joseph C Van Amburg Jessica Ulloa Alexander J Silver Leo Y Luo Angela Jones Paul Brent Ferrell Ashwin Kishtagari Yaomin Xu Michael R Savona Alexander G Bick Vanderbilt Genetics Institute Vanderbilt University Nashville TN. Department of Medicine Queen's University Kingston Ontario. Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN. Department of Medicine Vanderbilt University Medical Center Nashville TN. Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN USA Department of Biostatistics Vanderbilt University Medical Center Nashville TN. Department of Medicine Vanderbilt University Medical Center Nashville TN USA Program in Cancer Biology Vanderbilt University School of Medicine Nashville TN. Department of Radiation Oncology Vanderbilt University Medical Center Nashville TN. Division of Hematology and Oncology Department of Medicine Vanderbilt University Medical Center Nashville TN. Vanderbilt Ingram Cancer Center Vanderbilt University Medical Center Nashville TN. Program in Cancer Biology Vanderbilt University School of Medicine Nashville TN USA Vanderbilt Ingram Cancer Center Vanderbilt University Medical Center Nashville TN USA Center for Immunobiology Vanderbilt University Medical Center Nashville TN. Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA Division of Genetic Medicine Vanderbilt University Medical Center Nashville Tennessee. alexander.bick@***.

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LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection

LiwTERM: A Lightweight Transformer-Based Model for Dermatolo...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Luis A. Souza André G. C. Pacheco Gabriel G. de Angelo Thiago Oliveira-Santos Christoph Palm João P. Papa Graduate Program of Informatics Federal University of Espírito Santo Vitória Brazil OTH Regensburg Regensburg Medical Image Computing (ReMIC) Regensburg Germany Department of Computing São Paulo State Univesity São Paulo Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Skin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient's clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient's history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.

关键词： Graphics Computational modeling Computer architecture Transformers Skin Lesions Clinical diagnosis Reliability History Skin cancer

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Positive Predictive Value of the Thumb-Palm Test for General Population Screening of Ascending Aortic Aneurysm

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The American journal of cardiology 2021年 161卷 116-117页

作者： James A Diao Gloria J Wu Arjun K Manrai Computational Health Informatics Program Boston Children's Hospital Boston Massachusetts Department of Biomedical Informatics Harvard Medical School Boston Massachusetts.

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Defining Radiology's Imaginarium

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Journal of the American College of Radiology 2022年第8期19卷 994-996页

作者： Slanetz, Priscilla J. Awan, Omer Cooke, Erin Gunderman, Richard Vice Chair Academic Affairs Department of Radiology and Associate Program Director Diagnostic Radiology Residency at Boston University Medical Center Boston Massachusetts Director Early Career Faculty Development and Co-Director Academic Writing Program for Boston University Medical Group Boston Massachusetts President Massachusetts Radiological Society Vice President Association of University Radiologists and Subspecialty Chair of the ACR Appropriateness Criteria Breast Imaging Panels Department of Radiology University of Maryland School of Medicine Baltimore Maryland Associate Vice Chair Education and Associate Program Director Radiology Residency Department of Diagnostic Radiology and Nuclear Medicine University of Maryland Medical Center Baltimore Maryland Co-Director Medical School Radiology Clerkship University of Maryland School of Medicine Baltimore Maryland Vice President Maryland Radiological Society Treasurer Radiology Research Alliance and Program Director SIIM Imaging Informatics Bootcamp Associate Program Director Diagnostic Radiology Residency and Director of Arts Department of Radiology Vanderbilt University Medical Center Nashville Tennessee and Chair PFCC ACR Art Subcommittee Department of Radiology Indiana University Health Indianapolis IN United States

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medical Knowledge-Enabled Multi-Task Learning for Gastric Cancer Survival Prediction

Medical Knowledge-Enabled Multi-Task Learning for Gastric Ca...

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IEEE International Symposium on Biomedical Imaging

作者： Degan Hao Qiong Li Yudong Zhang Shandong Wu Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA The First Affiliated Hospital With Nanjing Medical University Nanjing PR China Dept. of Radiology Dept. of Biomedical Informatics and Dept. of Bioengineering University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate cancer survival prediction can inform patient prognostication and treatment planning. Multi-source clinical data, including radiological images and pathological markers, demand effective machine learning strategies to make smart use of these data to enhance patient outcome. In this work, we proposed a novel strategy of translating multi-task learning to clinical applications for gastric cancer survival prediction, by leveraging the established medical knowledge of abdominal CT images and pathological staging markers to construct meaningful auxiliary tasks for joint learning with the target task. In implementing our method for survival prediction, we also customized a convolutional neural network to account for censored data. We evaluated our method with extensive experiments on a large cohort of 1,061 gastric cancer patients for both overall and progression-free survival prediction, showing superior performance when compared to multi-input learning and two state-of-the-art prediction methods. As an additional benefit, our method also enables survival prediction even before surgery. Source code is available at https://***/usernameoliver/***.

关键词： Pathology Accuracy Source coding Surgery Prediction methods Multitasking Planning

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User-Centered Evaluation of a Visual Annotation Tool for Rapid Assessment of Pediatric Weight Entry Errors 18

User-Centered Evaluation of a Visual Annotation Tool for Rap...

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18th World Congress on medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Wu, Danny T.Y. Van Camp, P.J. Kim, Abraham Parikh, Milan Liu, Lei Mahdi, Monifa Ni, Yizhao Spooner, S. Andrew Department of Biomedical Informatics University of Cincinnati College of Medicine 231 Albert Sabin Way CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Medical Sciences Baccalaureate Program University of Cincinnati College of Medicine CincinnatiOH United States Cincinnati Children's Hospital Medical Center CincinnatiOH United States

ISBN: (纸本)9781643682648

Weight entry errors can cause significant patient harm in pediatrics due to pervasive weight-based dosing practices. While computerized algorithms can assist in error detection, they have not achieved high sensitivity and specificity to be further developed as a clinical decision support tool. To train an advanced algorithm, expert-annotated weight errors are essential but difficult to collect. In this study, we developed a visual annotation tool to gather large amounts of expertly annotated pediatric weight charts and conducted a formal user-centered evaluation. Key features of the tool included configurable grid sizes and annotation styles. The user feedback was collected through a structured survey and user clicks on the interface. The results show that the visual annotation tool has high usability (average SUS=86.4). Different combinations of the key features, however, did not significantly improve the annotation efficiency and duration. We have used this tool to collect expert annotations for algorithm development and benchmarking. © 2022 International medical informatics Association (IMIA) and IOS Press.

关键词： Data visualization

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Low-Cost Air Box Application for Indoor Environment Classification and LSTM Microclimate Prediction

Low-Cost Air Box Application for Indoor Environment Classifi...

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International Conference on Control and Robots (ICCR)

作者： Hu Hsun-Jui Chiu Yu-Hsien Yang Pei-Ching Master Degree Program in Information and Design Chang Jung Christian University Tainan Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering Chang Jung Christian University Tainan Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage.

关键词： Temperature measurement Temperature sensors Atmospheric modeling Predictive models Air quality Data models Forecasting Long short term memory Monitoring Indoor air quality

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Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

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Sensors 2023年第22期23卷 9199-9199页

作者： Chen, Shuo-Tsung Ye, Ren-Jie Wu, Tsung-Hsien Cheng, Chun-Wen Zhan, Po-You Chen, Kuan-Ming Zhong, Wan-Yu Department of Medical Informatics Chung Shan Medical University Taichung40201 Taiwan Department of Information Center Chung Shan Medical University Hospital Taichung40201 Taiwan Graduate School of Applied Chinese Studies National Yunlin University of Science and Technology Yunlin 64002 Taiwan Bachelor’s Program in Business Management Fu Jen Catholic University New Taipei City242062 Taiwan

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. © 2023 by the authors.

关键词： Electrocardiograms

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Genomic data processing with GenomeFlow

arXiv

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arXiv 2025年

作者： Park, Junseok Maury, Eduardo A. Oh, Changhoon Shin, Donghoon Denisko, Danielle Lee, Eunjung Alice Division of Genetics and Genomics Boston Children’s Hospital 3 Blackfan Circle BostonMA United States Department of Pediatrics Harvard Medical School 25 Shattuck St BostonMA United States Bioinformatics Integrative Genomics Program Harvard/MIT MD-PHD Program Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Graduate School of Information Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul Korea Republic of Department of Human Centered Design and Engineering University of Washington SeattleWA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure services on cloud computing platforms enable the processing of such large-scale genomic sequence data in distributed processing environments with a significant reduction in analysis time. However, parallel processing on cloud computing platforms presents many challenges to researchers, even skillful bioinformaticians. In particular, it is difficult to design a computing architecture optimized to reduce the cost of computing and disk storage as genomic data analysis pipelines often employ many heterogeneous tools with different resource requirements. To address these issues, we developed GenomeFlow, a tool for automated development of computing architecture and resource optimization on Google Cloud Platform, which allows users to process a large number of samples at minimal cost. We outline multiple use cases of GenomeFlow demonstrating its utility to significantly reduce computing time and cost associated with analyzing genomic and transcriptomic data from hundreds to tens of thousands of samples from several consortia. Here, we describe a step-by-step protocol on how to use GenomeFlow for a common genomic data processing task. We introduce this example protocol geared toward a bioinformatician with little experience in cloud computing. © 2025, CC BY.

关键词： Cloud platforms

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CORNN: convex optimization of recurrent neural networks for rapid inference of neural dynamics 23

CORNN: convex optimization of recurrent neural networks for ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Fatih Dinc Adam Shai Mark J. Schnitzer Hidenori Tanaka Department of Applied Physics Stanford University Stanford CA CNC Program Stanford University Stanford CA Howard Hughes Medical Institute CNC Program Stanford University Stanford CA Physics & Informatics Laboratories NTT Research Inc. Sunnyvale CA and Center for Brain Science Harvard University Cambridge MA

Advances in optical and electrophysiological recording technologies have made it possible to record the dynamics of thousands of neurons, opening up new possibilities for interpreting and controlling large neural populations in behaving animals. A promising way to extract computational principles from these large datasets is to train data-constrained recurrent neural networks (dRNNs). Performing this training in real-time could open doors for research techniques and medical applications to model and control interventions at single-cell resolution and drive desired forms of animal behavior. However, existing training algorithms for dRNNs are inefficient and have limited scalability, making it a challenge to analyze large neural recordings even in offline scenarios. To address these issues, we introduce a training method termed Convex Optimization of Recurrent Neural Networks (CORNN). In studies of simulated recordings, CORNN attained training speeds ~ 100-fold faster than traditional optimization approaches while maintaining or enhancing modeling accuracy. We further validated CORNN on simulations with thousands of cells that performed simple computations such as those of a 3-bit flip-flop or the execution of a timed response. Finally, we showed that CORNN can robustly reproduce network dynamics and underlying attractor structures despite mismatches between generator and inference models, severe subsampling of observed neurons, or mismatches in neural time-scales. Overall, by training dRNNs with millions of parameters in subminute processing times on a standard computer, CORNN constitutes a first step towards real-time network reproduction constrained on large-scale neural recordings and a powerful computational tool for advancing the understanding of neural computation. CORNN software and reproduction code are available at https://***/schnitzer-lab/CORNN-public

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