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arXiv 2023年

作者： Li, Siqi Liu, Pinyan Nascimento, Gustavo G. Wang, Xinru Leite, Fabio Renato Manzolli Chakraborty, Bibhas Hong, Chuan Ning, Yilin Xie, Feng Teo, Zhen Ling Ting, Daniel Shu Wei Haddadi, Hamed Ong, Marcus Eng Hock Peres, Marco Aurélio Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore National Dental Research Institute Singapore National Dental Centre Singapore Singapore Oral Health Academic Clinical Programme Duke-NUS Medical School Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Singapore National Eye Centre Singapore Singapore Eye Research Institute Singapore Department of Computing Imperial College London London United Kingdom Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School 8 College Road Singapore169857 Singapore

Objectives Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. Materials and Methods We searched five databases, SCOPUS, MEDLINE, Web of science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Results Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. Conclusions The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research. © 2023, CC BY.

关键词： Clinical research

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Challenges and Experiences Extending the cBioPortal for Cancer Genomics to a Molecular Tumor Board Platform

Challenges and Experiences Extending the cBioPortal for Canc...

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2021 European Federation for Medical Informatics (EFMI) Special Topic Conference, STC 2021

作者： Reimer, Niklas Unberath, Philipp Busch, Hauke Börries, Melanie Metzger, Patrick Ustjanzew, Arsenij Renner, Christopher Prokosch, Hans-Ulrich Christoph, Jan Group for Medical Systems Biology Lübeck Institute of Experimental Dermatology Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Department of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Medical Bioinformatics and Systems Medicine Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Heidelberg Germany Johannes Gutenberg-University School of Medicine Mainz Germany Medical Data Science Martin-Luther-University Halle-Wittenberg Faculty of Medicine Halle Germany

ISBN: (纸本)9781643682365

In Molecular Tumor Boards (MTBs), therapy recommendations for cancer patients are discussed. To aid decision-making based on the patient's molecular profile, the research platform cBioPortal was extended based on users' requirements. Additionally, a comprehensive dockerized workflow was developed to support the deployment of cBioPortal and connected services. In this work, we present the challenges and experiences of nearly two years of implementing and deploying an MTB platform based on cBioPortal and compare those to findings of a previous study. © 2021 The European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved.

关键词： Genome

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AutoScore-ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes

arXiv

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arXiv 2022年

作者： Saffari, Seyed Ehsan Ning, Yilin Feng, Xie Chakraborty, Bibhas Volovici, Victor Vaughan, Roger Ong, Marcus Eng Hock Liu, Nan Centre For Quantitative Medicine Duke-NUS Medical School Singapore Singapore Programme In Health Services And Systems Research Duke-NUS Medical School Singapore Singapore Department Of Biostatistics And Bioinformatics Duke University DurhamNC United States Department Of Statistics And Data Science National University Of Singapore Singapore Singapore Department Of Neurosurgery Erasmus MC Stroke Center Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Department Of Public Health Erasmus University Rotterdam Netherlands Department Of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute Of Data Science National University Of Singapore Singapore Singapore

Background Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patients. AutoScore is a machine learning-based automatic clinical score generator for binary outcomes. This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes. Methods The AutoScore-Ordinal framework is generated using the same 6 modules of the original AutoScore algorithm including variable ranking, variable transformation, score derivation (from proportional odds models), model selection, score fine-tuning, and model evaluation. To illustrate the AutoScore-Ordinal performance, the method was conducted on electronic health records data from the emergency department at Singapore General Hospital over 2008 to 2017. The model was trained on 70% of the data, validated on 10% and tested on the remaining 20%. Results This study included 445,989 inpatient cases, where the distribution of the ordinal outcome was 80.7% alive without 30-day readmission, 12.5% alive with 30-day readmission, and 6.8% died inpatient or by day 30 post discharge. Two point-based risk prediction models were developed using two sets of 8 predictor variables identified by the flexible variable selection procedure. The two models indicated reasonably good performance measured by mean area under the receiver operating characteristic curve (0.785 and 0.793) and generalized c-index (0.737 and 0.760), which were comparable to alternative models. Conclusion AutoScore-Ordinal provides an automated and easy-to-use framework for development and validation of risk prediction models for ordinal outcomes, which can systematically identify potential predictors from high-dimensional data. © 2022, CC BY.

关键词： Records management

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Classification of Breast cancer lesions in ultrasound images by using attention layer and loss ensembles in deep convolutional neural networks

arXiv

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arXiv 2021年

作者： Kalafi, Elham Yousef Jodeiri, Ata Setarehdan, Seyed Kamaledin Lin, Ng Wei Rahman, Kartini Binti Taib, Nur Aishah Dhillon, Sarinder Kaur Data Science & Bioinformatics Laboratory Institute of Biological Sciences Faculty of Science University of Malaya Kuala Lumpur Malaysia School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Biomedical Imaging Faculty of Medicine University of Malaya Kuala Lumpur50603 Malaysia Department of Surgery Faculty of Medicine University of Malaya Kuala Lumpur Malaysia

Reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low cost method for early diagnosis of breast cancer. The accuracy of the diagnosis is however highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The leverage in deep convolutional neural network approaches provided solutions in efficient analysis of breast ultrasound images. In this study, we proposed a new framework for classification of breast cancer lesions by use of an attention module in modified VGG16 architecture. We also proposed new ensembled loss function which is the combination of binary cross-entropy and logarithm of the hyperbolic cosine loss to improve the model discrepancy between classified lesions and its labels. Networks trained from pretrained ImageNet weights, and subsequently fine-tuned with ultrasound datasets. The proposed model in this study outperformed other modified VGG16 architectures with the accuracy of 93% and also the results are competitive with other state of the art frameworks for classification of breast cancer lesions. In this study, we employed transfer learning approaches with the pre-trained VGG16 architecture. Different CNN models for classification task were trained to predict benign or malignant lesions in breast ultrasound images. Our Experimental results show that the choice of loss function is highly important in classification task and by adding an attention block we could empower the performance our model. © 2021, CC BY.

关键词： Deep neural networks

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How can mathematics be used to improve burn care?

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Burns Open 2025年 9卷 100390-100390页

作者： Egberts, Ginger Vermolen, Fred Peng, Qiyao Korkmaz, H.Ibrahim van Zuijlen, Paul Computational Mathematics Group Department of Mathematics and Statistics University of Hasselt Diepenbeek Belgium Dutch Burn Foundation Beverwijk Netherlands Data Science Institute (DSI) University of Hasselt Hasselt Belgium Mathematical Institute University of Leiden Leiden Netherlands Burn Center Department of Plastic Reconstructive and Hand Surgery Red Cross Hospital Beverwijk Netherlands Department of Plastic Reconstructive and Hand Surgery Amsterdam UMC Location VUmc Amsterdam Movement Sciences Amsterdam Netherlands Department of Molecular Cell Biology and Immunology Amsterdam UMC Location VUmc Amsterdam Netherlands Pediatric Surgical Centre Emma Children's Hospital Amsterdam UMC Location AMC and VUmc Amsterdam Netherlands Bioinformatics Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

Severe second-degree ‘partial thickness’ and third-degree ‘full thickness’ burns are characterized by damage to the dermal layer of the skin. In the dermis, specialized cells called fibroblasts play a crucial role in wound healing. These cells produce collagen, a protein that provides strength and structure to the skin. After burn injury, fibroblasts migrate to the injured area and start producing and depositing collagen to help repair the damaged tissue. While contraction is essential for closing the wound, it can also result in scar contraction (contractures), especially in more severe burns. This contraction creates stresses on the skin, which can deteriorate the mobility of joints near the burn. This article overviews the most recent research results in computer simulations of scar contraction after burns. © 2024 The Authors

关键词： Agent-based models Artificial intelligence Continuum models Mathematical models Mathematics in the clinic Parameter uncertainty Post-burn contraction

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A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history (vol 29, pg 761, 2022)

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2022年第9期29卷 1657-1657页

作者： Maurits, Marc P. Korsunsky, Ilya Raychaudhuri, Soumya Murphy, Shawn N. Smoller, JordanW Weiss, Scott T. Petukhova, Lynn M. Weng, Chunhua Wei, Wei-Qi Huizinga, Thomas W. J. Reinders, Marcel J. T. Karlson, Elizabeth W. van den Akker, Erik B. Knevel, Rachel Department of Rheumatology Leiden University Medical Center Leiden The Netherlands. Leiden Computational Biology Center Leiden University Medical Center Leiden The Netherlands. Center for Data Sciences Brigham and Women's Hospital Harvard Medical School Boston MA USA. Research Information Science and Computing Mass General Brigham Boston MA USA. Center for Precision Psychiatry Department of Psychiatry Massachusetts General Hospital Boston MA USA. Psychiatric and Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. The Delft Bioinformatics Lab Delft University of Technology Delft The Netherlands. Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Harvard Medical School Boston MA USA. Section of Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands.

OBJECTIVE: To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. MATERIAL AND METHODS: We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. RESULTS: We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. DISCUSSION: Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. CONCLUSION: We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

关键词： ICD PhenoGraph clustering eMERGE electronic health records electronic medical records

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Sleep Quality and Daily Activity Association Assessment From Wearable Device data

Sleep Quality and Daily Activity Association Assessment From...

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International Conference on Information Management and Technology (ICIMTech)

作者： Bharuno Mahesworo Arif Budiarto Alam Ahmad Hidayat Haryono Soeparno Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Having good sleep is important to maintain physical and mental health. However, many young adults suffer poor sleep due to work or academic demands. Even more, sleep is associated with stress which is many young adults experienced it. To solve this problem, many epidemiologic studies suggest that exercise may improve sleep quality. On the other hand, several studies show that daily activity has no association with sleep quality. In this study, we tried to find more evidence of whether the daily activity has an association with sleep quality or not. We used a commercially available wearable device to capture the subject's heart rate, step count and deep sleep duration for 7 months. Then we used linear regression, XGBoost, and Long Short-Term Memory to find an association between these variables. The results show that there was no association between these variables.

关键词： Sleep Heart rate Biomedical monitoring Computer science Predictive models bioinformatics data science

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Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference

arXiv

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arXiv 2021年

作者： Lundén, Daniel Öhman, Joey Kudlicka, Jan Senderov, Viktor Ronquist, Fredrik Broman, David EECS and Digital Futures KTH Royal Institute of Technology Stockholm Sweden AI Sweden Stockholm Sweden Department of Data Science and Analytics BI Norwegian Business School Oslo Norway Department of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden Department of Zoology Stockholm University Stockholm Sweden

Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling probabilistic checkpoints in PPLs through continuation-passing style transformations or non-preemptive multitasking—as is done in many popular PPLs—often disallows compilation to low-level languages required for high-performance platforms such as GPUs. To solve the checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs)—a simple and efficient approach to checkpoints in low-level languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs and illustrate its application when compiling Miking CorePPL—a high-level universal PPL—to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6× speedups over state-of-the-art PPLs implementing SMC inference. Copyright © 2021, The Authors. All rights reserved.

关键词： C++ (programming language)

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A Two-Stage Approach for Segmenting Spatial Point Patterns Applied to Multiplex Imaging

arXiv

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arXiv 2024年

作者： Sheng, Alvin Reich, Brian J. Staicu, Ana-Maria Krishnan, Santhoshi N. Rao, Arvind Frankel, Timothy L. Division of Biostatistics and Health Data Science University of Minnesota MinneapolisMN United States Department of Statistics North Carolina State University RaleighNC United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Biostatistics University of Michigan Ann ArborMI United States Department of Radiation Oncology University of Michigan Ann ArborMI United States Department of Biomedical Engineering University of Michigan Ann ArborMI United States Department of Surgery University of Michigan Ann ArborMI United States

Recent advances in multiplex imaging have enabled researchers to locate different types of cells within a tissue sample. This is especially relevant for tumor immunology, as clinical regimes corresponding to different stages of disease or responses to treatment may manifest as different spatial arrangements of tumor and immune cells. Spatial point pattern modeling can be used to partition multiplex tissue images according to these regimes. To this end, we propose a two-stage approach: first, local intensities and pair correlation functions are estimated from the spatial point pattern of cells within each image, and the pair correlation functions are reduced in dimension via spectral decomposition of the covariance function. Second, the estimates are clustered in a Bayesian hierarchical model with spatially-dependent cluster labels. The clusters correspond to regimes of interest that are present across subjects;the cluster labels segment the spatial point patterns according to those regimes. Through Markov Chain Monte Carlo sampling, we jointly estimate and quantify uncertainty in the cluster assignment and spatial characteristics of each cluster. Simulations demonstrate the performance of the method, and it is applied to a set of multiplex immunofluorescence images of diseased pancreatic tissue. Copyright © 2024, The Authors. All rights reserved.

关键词： Tumors

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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