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14th International Conference on Semantic Web Applications and Tools for health Care and Life sciences, SWAT4HCLS 2023

作者： Gouthamchand, Varsha Choudhury, Ananya Hoebers, Frank Wesseling, Frederik Welch, Mattea Kim, Sejin Haibe-Kains, Benjamin Kazmierska, Joanna Dekker, Andre van Soest, Johan Wee, Leonard GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht Netherlands Clinical Data Science Faculty of Health Medicine and Life Sciences Maastricht University Maastricht Netherlands Radiation Medicine Program Princess Margaret Cancer Centre University Health Network TorontoON Canada Dept of Radiation Oncology Greater Poland Cancer Centre II Poznan Poland Brightlands Institute for Smart Society Faculty of Science and Engineering Maastricht University Heerlen Netherlands

Research in modern healthcare requires vast volumes of data from various healthcare centers across the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated Learning approach, governed in a set of agreements such as the Personal health Train (PHT) manages to tackle these concerns by distributing models to the data centers instead of the traditional approach of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles help in building interoperable and reusable data by adding knowledge representation and providing descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward. Our main objective is to disentangle this process by using domain and technical expertise and get data prepared for federated learning. This paper introduces applications that are easily deployable as Docker containers, which will automate parts of the aforementioned process and significantly simplify the task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to explore data from multiple centers without breaching privacy. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge representation

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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IEEE International Conference on Big data

作者： Ruofan Hu Dongyu Zhang Dandan Tao Huayi Zhang Hao Feng Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose USA College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and health science 2024年第5期13卷 611-620页

作者： Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA Translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego Moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction ***:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:Measured VO_(2max)is a strong predictor of *** ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness Prediction algorithms Epidemiology Mortality

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The largest genome-wide association study for breast cancer in Taiwanese Han population

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Breast Cancer Research and Treatment 2024年第2期203卷 291-306页

作者： Hsu, Yu-Ching Chen, Hung-Lin Cheng, Chi-Fung Chattopadhyay, Amrita Chen, Pei-Shan Lin, Che-Chen Chiang, Hsiu-Yin Liu, Ting-Yuan Huang, Chi-Hao Kuo, Chin-Chi Chuang, Eric Y. Lu, Tzu-Pin Tsai, Fuu-Jen Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Center for Translational Genomic Research Department of Medical Research China Medical University Hospital Taichung Taiwan Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Breast Surgery Department of Surgery China Medical University Hospital Taichung Taiwan Division of Nephrology Department of Internal Medicine China Medical University Hospital Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan

Purpose: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. Methods: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. Results: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79–7.66) and 3.55 (2.13–6.14), respectively. Conclusion: In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Breast cancer Genome-wide association study Polygenic risk score Taiwanese population

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When data disappear: public health pays as US policy strays

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The Lancet Digital health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： health insurance

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

arXiv

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arXiv 2023年

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Zhang, Huayi Feng, Hao Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute Worcester United States Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose United States College of Ag&Environ Sciences North Carolina A&T State University Greensboro United States Computer Science/Data Science Program Worcester Polytechnic Institute Worcester United States

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a class-imbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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Assessing high-order effects in feature importance via predictability decomposition

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Physical Review E 2025年第3期111卷 L033301-L033301页

作者： Marlis Ontivero-Ortega Luca Faes Jesus M. Cortes Daniele Marinazzo Sebastiano Stramaglia Dipartimento Interateneo di Fisica Università degli Studi di Bari Aldo Moro and INFN Sezione di Bari 70126 Bari Italy Dipartimento di Ingegneria Università di Palermo 90128 Palermo Italy Faculty of Technical Sciences University of Novi Sad 21000 Novi Sad Serbia Biocruces-Bizkaia Health Research Institute 48903 Barakaldo Spain Biomedical Research Doctorate Program University of the Basque Country 48940 Leioa Spain Department of Cell Biology and Histology University of the Basque Country 48940 Leioa Spain IKERBASQUE Basque Foundation for Science 48009 Bilbao Spain Department of Data Analysis Ghent University 9000 Ghent Belgium

Building on recent advances in describing redundancy and synergy in multivariate interactions among random variables, we propose an approach to quantify cooperative effects in feature importance, a key technique in explainable artificial intelligence. Specifically, we introduce an adaptive version of the widely used metric Leave One Covariate Out (LOCO), designed to disentangle high-order effects involving a particular input feature in regression problems. LOCO measures the reduction in prediction error when the feature of interest is added to the set of features used in regression. Unlike the standard approach that computes LOCO using all available features, our method identifies the subsets of features that maximize and minimize LOCO. This results in a decomposition of LOCO into a two-body component and higher-order components (redundant and synergistic), while also identifying the features that contribute to these high-order effects in conjunction with the driving feature. We demonstrate the effectiveness of the proposed method in a benchmark dataset related to wine quality and to proton versus pion discrimination using simulated detector measurements generated by GEANT.

关键词： Regression analysis

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Population simulation modeling of disparities in US breast cancer mortality

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Journal of the National Cancer Institute - Monographs 2023年第62期2023卷 178-187页

作者： Mandelblatt, Jeanne S. Schechter, Clyde B. Stout, Natasha K. Huang, Hui Stein, Sarah Hunter Chapman, Christina Trentham-Dietz, Amy Jayasekera, Jinani Gangnon, Ronald E. Hampton, John M. Abraham, Linn O’Meara, Ellen S. Sheppard, Vanessa B. Lee, Sandra J. Department of Oncology Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center WA United States Departments of Family and Social Medicine and of Epidemiology and Population Health Albert Einstein College of Medicine Bronx NY United States Department of Population Sciences Harvard Medical School and Harvard Pilgrim Health Care Institute Boston MA United States Department of Data Science Dana-Farber Cancer Institute and Harvard Medical School Boston MA United States Department of Radiation Oncology Section of Health Services Research Baylor College of Medicine and Health Policy Quality and Informatics Program at the Center for Innovations in Quality Effectiveness and Safety Michael E. DeBakey VA Medical Center Houston TX United States Department of Population Health Sciences and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Health Equity and Decision Sciences Research Lab National Institute on Minority Health and Health Disparities Intramural Research Program National Institutes of Health Bethesda MD United States Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Kaiser Permanente Washington Health Research Institute Seattle WA United States Department of Health Behavior and Policy and Massey Cancer Center Virginia Commonwealth University Richmond VA United States

BACKGROUND: Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. METHODS: Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. RESULTS: Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. CONCLUSION: Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***

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Cryptotanshinone Suppresses the STAT3/BCL-2 Pathway to Provoke Human Bladder Urothelial Carcinoma Cell Death

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Environmental Toxicology 2025年第4期40卷 624-635页

作者： Tung, Min-Che Chang, Ge-Man Dai, Wen-Chyi Hsu, Chen-Hsuan Chang, Hsiang-Chun Yang, Wei-Ting Ho, Yann-Jen Lu, Chien-Hsing Chen, Yi-Hsin Chang, Chia-Che Division of Urology Department of Surgery Tungs' Taichung MetroHarbor Hospital Taichung Taiwan Graduate Institute of Biomedical Sciences National Chung Hsing University Taichung Taiwan Doctoral Program in Biotechnology Industrial Innovation and Management National Chung Hsing University Taichung Taiwan Department of Life Sciences National Chung Hsing University Taichung Taiwan Department of Obstetrics and Gynecology Taichung Veterans General Hospital Taichung Taiwan Doctoral Program in Translational Medicine National Chung Hsing University Taichung Taiwan Department of Nephrology Taichung Tzu Chi Hospital Buddhist Tzu chi Medical Foundation Taichung Taiwan School of Medicine Tzu Chi University Hualein Taiwan Department of Artificial Intelligence and Data Science National Chung Hsing University Taichung Taiwan Master Program in Precision Health Rong Hsing Research Center for Translational Medicine The iEGG and Animal Biotechnology Research Center National Chung Hsing University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan

Bladder cancer is one of the most common human malignancies worldwide. Aberrant activation of signal transducer and activator of transcription 3 (STAT3) is crucial to driving malignant progression and predicting poor prognosis of multiple human cancers, including bladder cancer, making STAT3 a promising target of cancer therapeutics. Cryptotanshinone (CTS) is an anticancer ingredient of Danshen (Salvia miltiorrhiza), a top-graded Chinese medicinal herb. However, whether CTS targets STAT3 to exert its cytotoxic effect on human bladder cancer remains unknown. Herein, we demonstrated that CTS is cytotoxic to multiple human urinary bladder transitional cell carcinoma (TCC) cell lines while sparing normal human urothelial cells. CTS provoked apoptosis-dependent bladder TCC cytotoxicity, as apoptosis blockage by z-VAD-fmk markedly rescued the clonogenicity of CTS-treated cells. Besides, CTS was found to suppress constitutive and interleukin 6-inducible activation of STAT3, evidenced by the downregulation of STAT3 tyrosine 705 phosphorylation and BCL2, a recognized STAT3 transcriptional target. Notably, ectopic expression of a dominant-active STAT3 mutant (STAT3-C) or BCL-2 alleviated CTS-induced apoptosis and clonogenicity inhibition, thus confirming STAT3 blockade as a pivotal mechanism of CTS's cytotoxic action on bladder TCC cells. Lastly, immunoblotting revealed that CTS lowered the levels of active JAK2, an upstream kinase that mediates STAT3 tyrosine 705 phosphorylation. Altogether, we conclude that the blockade of the JAK2/STAT3/BCL-2 antiapoptotic signaling axis is a vital mechanism whereby CTS provokes bladder cancer cytotoxicity. The current evidence implicates CTS's potential to be translated into a bladder cancer therapeutic agent. © 2024 Wiley Periodicals LLC.

关键词： Cell death

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Surgical Instrument Detection for Laparoscopic Surgery using Deep Learning 19

Surgical Instrument Detection for Laparoscopic Surgery using...

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19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Boonkong, Apiwat Hormdee, Daranee Sonsilphong, Suphachoke Khampitak, Kovit Khon Kaen University Embedded System RandD Group Computer Engineering Khon Kaen Thailand Khon Kaen University Data Science and Health Informatics Program Academic Affairs Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kaen Thailand

ISBN: (纸本)9781665485845

Laparoscopic surgery has transformed conventional open surgery. Robot-Assisted laparoscopic surgery which is minimally invasive is effective for operations in limited space. Nevertheless, the robotic system which is utilized in such surgery can inflict impair to the surgical instruments, organs, or tissues as a consequence of the narrow field of view and space available to operate, and insufficient tactile response. In laparoscopy, the surgeon observes what he operates via digital images which are displayed on a monitor. In addition to the surgeon's expertise, the image-guided surgery system could offer him significant support if that system is capable of understanding those images. Computer Vision has been able to proceed due to the emergence of Machine Learning, and especially Deep Learning. This paper applies Deep Learning for surgical instrument detection for laparoscopic surgery. The fastest model, based on results trained on the COCO 2017 dataset on TensorFlow Framework, for each of 4 algorithms-Faster-RCNN, SSD, CenterNet and EfficientDet-from Model Zoo, along with YOLOv4 on Darknet Framework, have been investigated in this work. The evaluation metrics used are Precision, Recall and F1-score. The results reveal that each model plays its part well;however, YOLOv4 seemed to breakthrough this statement by offering grate result for all three accuracy metrics. © 2022 IEEE.

关键词： Laparoscopy

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