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arXiv 2024年

作者： Yuan, Hao Mancuso, Christopher A. Johnson, Kayla Braasch, Ingo Krishnan, Arjun Genetics and Genome Science Program Ecology Evolution and Behavior Program Michigan State University United States Department of Biostatistics & Informatics University of Colorado Anschutz Medical Campus United States Department of Biomedical Informatics University of Colorado Anschutz Medical Campus United States Department of Integrative Biology Genetics and Genome Science Program Ecology Evolution and Behavior Program Michigan State University United States

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer. © 2024, CC BY.

关键词： Biotic

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Machine Learning Model Deployment Using Real-Time Physiological Monitoring: Use Case of Detecting Delayed Cerebral Ischemia

Machine Learning Model Deployment Using Real-Time Physiologi...

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2022 IEEE-EMB Special Topic Conference on Healthcare Innovations and Point of Care Technologies, HI-POCT 2022

作者： Cao, Hanqing Finer, Joshua Megjhani, Murad Nametz, Daniel C. Lorenzi, Virginia Mamykina, Lena Meyers, Richard Rossetti, Sarah C. Park, Soojin New York-Presbyterian Hospital New YorkNY United States Columbia University Irving Medical Center Department of Neurology Program for Hospital and Intensive Care Informatics Department of Neurology Biomedical Informatics New YorkNY United States Columbia University Irving Medical Center Department of Biomedical Informatics School of Nursing New YorkNY United States

ISBN: (纸本)9781665496155

Deploying machine learning models in a clinical setting is challenging. Here we demonstrated a modular model deployment pipeline for for ContinuOuS Monitoring tool for delayed cerebral IsChemia (COSMIC) that was successfully implemented on a trained and validated temporal machine learning algorithm that detects delayed cerebral ischemia after subarachnoid hemorrhage. The pipeline was able to ingest demographic data and near real-time continuous physiological data, run through a model, and output the results twice a day automatically. It was a highly collaborative effort among clinical neurologists, the research team, and the IT innovation team. We illustrate the technical run time challenges and mitigations in each of the three components of the pipeline: data, model (modular), and output communication. Future work is user-centered participatory design and rapid agile prototyping of effective model output communication and clinical trial of efficacy in order to understand how the clinical decision support tool performs and is adopted in a real clinical setting. © 2022 IEEE.

关键词： Pipelines

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Single-sequence protein structure prediction using a language model and deep learning (Oct, 10.1038/s41587-022-01432-w, 2022)

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NATURE BIOTECHNOLOGY 2022年第11期40卷 1692-1692页

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Floristean, Christina Kharkar, Anant Roy, Koushik Rochereau, Charlotte Ahdritz, Gustaf Zhang, Joanna Church, George M. Sorger, Peter K. AlQuraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Nabla Bio Inc. Boston MA USA Department of Computer Science Columbia University New York NY USA Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New York NY USA Department of Systems Biology Columbia University New York NY USA Department of Systems Biology Harvard Medical School Boston MA USA

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

关键词： Computational biophysics Machine learning Protein structure predictions

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation 48

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications Taiwan

ISBN: (纸本)9781728163277

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems"using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). © 2023 IEEE.

关键词： Inertial measurement units Knowledge distillation Pre-impact fall detection Vision transformer Wearable sensors

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Model-Based Insulin Sensitivity and Beta-Cell Function Estimation from Daily Continuous Glucose Monitoring

Model-Based Insulin Sensitivity and Beta-Cell Function Estim...

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

作者： Elizabeth Healey Isaac Kohane Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment decisions. However, the utility of CGM in management of T2D remains an active area of research. In our work, we demonstrate the feasibility of exploiting raw daily CGM data to estimate the physiological parameters of insulin sensitivity and beta-cell function that correlate with estimates derived from laboratory findings. We use a peak extraction algorithm to extract peaks from daily CGM data and implement a model-based approach to infer physiological parameters. We demonstrate that the inferred parameter estimates of insulin sensitivity and beta-cell function correlate to the ground truth measurements as determined by an oral glucose tolerance test (OGTT).

关键词： Sensitivity Biological system modeling Estimation Data models Diabetes Data mining Insulin Engineering in medicine and biology

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Actigraphy-derived circadian rhythms, sleep-wake patterns, and physical activity across clinical stages and pathophysiological subgroups in young people presenting for mental health care

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Journal of Psychiatric Research 2025年 186卷 396-406页

作者： Carpenter, Joanne S. Crouse, Jacob J. Park, Shin Ho Shin, Mirim Tonini, Emiliana Guo, Wei Merikangas, Kathleen R. Iorfino, Frank Leroux, Andrew Nichles, Alissa Zmicerevska, Natalia Scott, Jan Scott, Elizabeth M. Hickie, Ian B. Youth Mental Health and Technology Team Brain & Mind Centre The University of Sydney NSW Australia Genetic Epidemiology Branch Intramural Research Program National Institute of Mental Health Bethesda United States Department of Biostatistics and Informatics University of Colorado Anschutz Medical Campus Aurora CO United States Institute of Neuroscience University of Newcastle Newcastle United Kingdom

Staging models for youth mental health aim to locate clinical presentations on a spectrum from at-risk states to persistent disorder and predict future illness trajectories. Our previous publications on trans-diagnostic staging proposed three pathophysiological subgroups of major mood or psychotic disorders in youth (‘hyperarousal-anxious depression’, ‘circadian-bipolar spectrum’, ‘neurodevelopmental-psychosis’). This study aims to investigate differences in objective measures of 24hr sleep-wake patterns, circadian rhythms, and physical activity across clinical stages and pathophysiological subgroups. Actigraphy data (median: 13 days) was collected from 497 youth presenting for mental health care (21.6 ± 4.7 years, 37% male) and 88 controls (24.1 ± 3.8 years, 44% male). Actigraphy estimates were compared across groups using analysis of covariance adjusting for age and sex. Compared with controls or earlier clinical stages, later clinical stages were significantly associated with longer sleep duration(η² = 0.04), later sleep midpoint(η² = 0.02), lower sleep regularity(η² = 0.02), lower relative amplitude of the rest-activity cycle(η² = 0.05), higher interdaily stability(η² = 0.03), lower total activity(η² = 0.08) and less moderate-vigorous physical activity(η² = 0.06). Compared to controls, the ‘circadian-bipolar spectrum’ subgroup had later sleep midpoint(η² = 0.02), and higher interdaily stability(η² = 0.03);the ‘neurodevelopmental-psychosis’ subgroup had longer sleep duration(η² = 0.02), and lower total activity(η² = 0.03);and the ‘hyperarousal-anxious depression’ subtype had later sleep midpoint(η² = 0.02), and lower sleep regularity(η² = 0.02). The findings suggest differences in sleep-wake and rest-activity patterns according to clinical stage and proposed illness trajectory subtypes. The cross-sectional associations of

关键词： Actigraphy Bipolar disorder Circadian rhythms Clinical stage Depression Mood disorders Physical activity Sleep

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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U-Netmer: U-Net Meets Transformer for medical Image Segmentation

SSRN

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SSRN 2024年

作者： He, Sheng Bo, Rina Grant, P. Ellen Ou, Yangming Boston Children’s Hospital Harvard Medical School 300 Longwood Ave. BostonMA United States Computational Health Informatics Program

The combination of the U-Net based deep learning models and Transformer is a new trend for medical image segmentation. U-Net can extract the detailed local semantic and texture information and Transformer can learn the long-rang dependencies among pixels in the input image. However, directly adapting the Transformer for segmentation has "token-flatten" problem (flattens the local patches into 1D tokens which losses the interaction among pixels within local patches) and "scale-sensitivity" problem (uses a fixed scale to split the input image into local patches). Compared to directly combining U-Net and Transformer, we propose a new global-local fashion combination of U-Net and Transformer, named U-Netmer, to solve the two problems. The proposed U-Netmer splits an input image into local patches. The global-context information among local patches is learnt by the self-attention mechanism in Transformer and U- Net segments each local patch instead of flattening into tokens to solve the ‘token-flatten" problem. The U-Netmer can segment the input image with different patch sizes with the identical structure and the same parameter. Thus, the U-Netmer can be trained with different patch sizes to solve the "scale-sensitivity" problem. We conduct extensive experiments in 7 public datasets on 7 organs (brain, heart, breast, lung, polyp, pancreas and prostate) and 4 imaging modalities (MRI, CT, ultrasound, and endoscopy) to show that the proposed U-Netmer can be generally applied to improve accuracy of medical image segmentation. These experimental results show that U-Netmer provides state-of-the-art performance compared to baselines and other models. In addition, the discrepancy among the outputs of U-Netmer with different scales is linearly correlated to the segmentation accuracy which can be considered as a confidence score to rank test images by difficulty without ground-truth. The code will be available on GitHub. © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal medical Image Segmentation Model for Highly Constrained Environments

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Med...

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International Challenge on Segment Anything in medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9783031818530

medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： medical imaging

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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