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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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A web-based, hospital-wide health care-associated bloodstream infection surveillance and classification system: Development and evaluation

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JMIR Medical informatics 2015年第3期3卷 e31页

作者： Tseng, Yi-Ju Wu, Jung-Hsuan Lin, Hui-Chi Chen, Ming-Yuan Ping, Xiao-Ou Sun, Chun-Chuan Shang, Rung-Ji Sheng, Wang-Huei Chen, Yee-Chun Lai, Feipei Chang, Shan-Chwen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Computational Health Informatics Program Boston Children's Hospital Boston MA United States Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Infection Control National Taiwan University Hospital Taipei Taiwan National Taiwan University Hospital Information Systems Office Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan National Health Research Institutes National Institute of Infectious Diseases and Vaccinology Miaoli Taiwan

Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20;r=.999 P<.001) and by time (n=14;r=.941;P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. © 2020 JMIR Publications Inc. All rights reserved.

关键词： Health care-associated infection Infection control Information systems Surveillance Web-based services

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Geographic and age variations in mutational processes in colorectal cancer

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Nature 2025年 1-11页

作者： Marcos Díaz-Gay Mariya Kazachkova Ammal Abbasi Christopher D. Steele Raviteja Vangara Erik N. Bergstrom Azhar Khandekar Burçak Otlu Ludmil B. Alexandrov Wellington dos Santos Sergey Senkin Behnoush Abedi-Ardekani Ana Carolina de Carvalho Thomas Cattiaux Ricardo Cortez Cardoso Penha Valérie Gaborieau Priscilia Chopard Sandra Perdomo Paul Brennan Sarah Moody Jingwei Wang Stephen Fitzgerald Saamin Cheema Calli Latimer Jon W. Teague Laura Humphreys Michael R. Stratton Christine Carreira Anush Mukeriya David Zaridze Riley Cox Monique Albert Larry Phouthavongsy Steven Gallinger Reza Malekzadeh Ahmadreza Niavarani Marko Miladinov Katarina Erić Sasa Milosavljevic Suleeporn Sangrajrang Maria Paula Curado Samuel Aguiar Rui Manuel Reis Monise Tadin Reis Luis Gustavo Romagnolo Denise Peixoto Guimarães Ivana Holcatova Jaroslav Kalvach Carlos Alberto Vaccaro Tamara Alejandra Piñero Beata Świątkowska Jolanta Lissowska Katarzyna Roszkowska-Purska Antonio Huertas-Salgado Tatsuhiro Shibata Satoshi Shiba Surasak Sangkhathat Taned Chitapanarux Gholamreza Roshandel Patricia Ashton-Prolla Daniel C. Damin Francine Hehn de Oliveira Trevor D. Lawley Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Moores Cancer Center University of California San Diego La Jolla CA USA Digital Genomics Group Structural Biology Program Spanish National Cancer Research Center (CNIO) Madrid Spain Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Health Informatics Graduate School of Informatics Middle East Technical University Ankara Turkey Sanford Stem Cell Institute University of California San Diego La Jolla CA USA Genomic Epidemiology Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Cancer Ageing and Somatic Mutation Wellcome Sanger Institute Cambridge UK Evidence Synthesis and Classification Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Clinical Epidemiology N. N. Blokhin National Medical Research Centre of Oncology Moscow Russia Ontario Tumour Bank Ontario Institute for Cancer Research Toronto Ontario Canada Centre for Biodiversity Genomics University of Guelph Guelph Ontario Canada Lunenfeld-Tanenbaum Research Institute Sinai Health System Toronto Ontario Canada Digestive Oncology Research Center Digestive Disease Research Institute Tehran University of Medical Sciences Tehran Iran Clinic for Digestive Surgery—First Surgical Clinic University Clinical Centre of Serbia Belgrade Serbia Department of Pathology University Clinical Centre of Serbia Belgrade Serbia International Organization for Cancer Prevention and Research Belgrade Serbia National Cancer Institute Bangkok Thailand Department of Epidemiology A. C. Camargo Cancer Center Sao Paulo Brazil Colon Cancer Reference Center A. C. Camargo Cancer Center Sao Paulo Brazil Molecular Oncology Research Center Barretos Cancer Hospital

Incidence rates of colorectal cancer vary geographically and have changed over time1. Notably, in the past two decades, the incidence of early-onset colorectal cancer, which affects individuals below 50 years of age, has doubled in many countries2,3,4,5. The reasons for this increase are unknown. Here we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite-unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown aetiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin6,7, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals who were diagnosed before 40 years of age than in those over 70 years of age, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked toAPCdriver mutations, with ID18 being responsible for about 25% ofAPCdriver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that mutagenic exposure to colibactin-producing bacteria in early life may contribute to the increasing incidence of early-onset colorectal cancer.

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Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning

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Journal of cellular and molecular medicine 2024年第12期28卷 e18404页

作者： Li-Jen Liao Chien-Chen Tsai Po-Yu Li Cheng-Yun Lee Shian-Ren Lin Wan-Yu Lai I-Yu Chen Chiung-Fang Chang Jan-Mou Lee Yen-Ling Chiu Department of Otolaryngology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Anatomical Pathology Far Eastern Memorial Hospital New Taipei City Taiwan. FullHope Biomedical Co. Ltd New Taipei City Taiwan. Division of Nephrology Department of Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Medical Research Far Eastern Memorial Hospital New Taipei City Taiwan. Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei Taiwan.

In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral blood remains unclear. In this study, we established an immune cell profile for patients with NPC and used flow cytometry and machine learning (ML) to identify the characteristics of this profile. After isolation of circulating leukocytes, the proportions of 104 immune cell subsets were compared between NPC group and the healthy control group (HC). Data obtained from the immune cell profile were subjected to ML training to differentiate between the immune cell profiles of the NPC and HC groups. We observed that subjects in the NPC group presented higher proportions of T cells, memory B cells, short-lived plasma cells, IgG-positive B cells, regulatory T cells, MHC II T cells, CTLA4 T cells and PD-1 T cells than subjects in the HC group, indicating weaker and compromised cellular and humoral immune responses. ML revealed that monocytes, PD-1 CD4 T cells, memory B cells, CTLA4 CD4 T cells and PD-1 CD8 T cells were strongly contributed to the difference in immune cell profiles between the NPC and HC groups. This alteration can be fundamental in developing novel immunotherapies for NPC.

关键词： flow cytometry immune cell profile machine learning nasopharyngeal carcinoma (NPC)

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Personal health records - Potential one-stop approach in polypharmacy

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Computer Methods and programs in Biomedicine Update 2022年 2卷

作者： Chen, Ya-Lin Satria, Fauzi Budi Jamshed, Shazia Babar, Zaheer-Ud-Din Mukherjee, Nilesh Yang, Hsuan-Chia Li, Yu-Chuan Iqbal, Usman Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan Department of Pharmacy Practice Faculty of Pharmacy UniSZA Kuala Terengganu Malaysia Qualitative Research-Methodological Application in Health Sciences Research Group Kulliyyah of Pharmacy International Islamic University Malaysia Kuantan Malaysia Centre for Pharmaceutical Policy and Practice Research Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Master of Science in Systems Biology Manipal School of Life Sciences Manipal Academy of Higher Education Manipal India International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation

arXiv

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arXiv 2024年

作者： Lee, Junhyeok Oh, Yujin Lee, Dahyoun Joh, Hyon Keun Sohn, Chul-Ho Baik, Sung Hyun Jung, Cheol Kyu Park, Jung Hyun Choi, Kyu Sung Kim, Byung-Hoon Ye, Jong Chul Interdisciplinary Program in Cancer Biology Seoul National University College of Medicine Seoul Korea Republic of Harvard Medical School BostonMA United States Department of Biomedical Systems Informatics Yonsei University College of Medicine Seoul Korea Republic of Department of Medicine The Graduate School of Yonsei University Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Department of Radiology Seoul National University Hospital Seoul Korea Republic of Department of Radiology Seoul National University Bundang Hospital Seongnam Korea Republic of Department of Radiology Seoul Metropolitan Government Seoul National University Boramae Medical Center Seoul Korea Republic of Department of Psychiatry Yonsei University College of Medicine Seoul Korea Republic of Institute of Behavioral Sciences in Medicine Yonsei University College of Medicine Seoul Korea Republic of Daejeon Korea Republic of

Acute ischemic stroke (AIS) requires time-critical management, with hours of delayed intervention leading to an irreversible disability of the patient. Since diffusion weighted imaging (DWI) using the magnetic resonance image (MRI) plays a crucial role in the detection of AIS, automated prediction of AIS from DWI has been a research topic of clinical importance. While text radiology reports contain the most relevant clinical information from the image findings, the difficulty of mapping across different modalities has limited the factuality of conventional direct DWI-to-report generation methods. Here, we propose paired image-domain retrieval and text-domain augmentation (PIRTA), a cross-modal retrieval-augmented generation (RAG) framework for providing clinician-interpretative AIS radiology reports with improved factuality. PIRTA mitigates the need for learning cross-modal mapping, which poses difficulty in image-to-text generation, by casting the cross-modal mapping problem as an in-domain retrieval of similar DWI images that have paired ground-truth text radiology reports. By exploiting the retrieved radiology reports to augment the report generation process of the query image, we show by experiments with extensive in-house and public datasets that PIRTA can accurately retrieve relevant reports from 3D DWI images. This approach enables the generation of radiology reports with significantly higher accuracy compared to direct image-to-text generation using state-of-the-art multimodal language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Erratum: Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

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biomedical Data Science 1000年第1期

作者： Ying Wang Kristin Tsuo Masahiro Kanai Benjamin M. Neale Alicia R. Martin 2Stanley Center for Psychiatric Research and Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts USA 1Analytic and Translational Genetics Unit Massachusetts General Hospital Boston Massachusetts USA email: armartin@*** 3Biological and Biomedical Sciences Harvard Medical School Boston Massachusetts USA 4Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA 5Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan

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Exploring disparities of teleconsultation readiness: A comparative analysis of healthcare facilities in Indonesia

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DIGITAL HEALTH 2024年 10卷

作者： Daniel Chriswinanto Adityo Nugroho Katherina Adisaputro Mitra Andini Sigilipoe Ida Ayu Triastuti Suryani Hutomo Ardhinta Bellano Septarda Aryososepti Yuwono Caraka Randi Yusuf Dedik Sulistiawan Balqis Gusbela Jason C Hsu Emily Chia-Yu Su Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta IndonesiaGraduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta Indonesia Faculty of Public Health Universitas Ahmad Dahlan Yogyakarta IndonesiaSchool of Public Health Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University TaipeiClinical Data Center Office of Data Science Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiResearch Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiInstitute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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