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BEYOND ACETABULAR DYSPLASIA AND PINCER MORPHOLOGY: REFINING HIP OSTEOARTHRITIS RISK ASSESSMENT THROUGH STATISTICAL SHAPE MODELING

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Osteoarthritis and Cartilage 2025年 33卷 S243-S244页

作者： Fleur Boel Noortje S. Riedstra Harbeer Ahedi Nigel Arden Myrthe A. van den Berg Michiel M. van Buuren Sita Bierma-Zeinstra Flavia Cicuttini Timothy F. Cootes Kay M. Crossley David Felson Willem Paul Giellis Josh Heerey Graeme Jones Stefan Kluzek Nancy E. Lane Claudia Lindner John A. Lynch Joyce v. Meurs Andrea Mosler Rintje Agricola Department of Orthopaedics and Sports Medicine Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Menzies Institute for Medical Research University of Tasmania Australia Tasmania Australia Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences Botnar Research Centre University of Oxford Oxford United Kingdom Department of General Practice Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Rheumatology Monash Universiyt/Alfred Hopstila Melbourne Victoria Australia Informatics Imaging and Data Science The University of Manchester Manchester United Kingdom La Trobe Sport and Exercise Medicine Research Centre School of Allied Health Human Services and Sport La Trobe University Melbourne Victoria Australia Section of Rheumatology Boston University School of Medicine Boston Massachusetts United States Department of Orthopedics University Medical Center Utrecht Utrecht Netherlands La Trobe Sport and Exercise Medicine Research Centre Melbourne Victoria Australia NIHR Biomedical Research Centre University of Nottingham Nottingham United Kingdom Medicine U.C. Davis Health Hillsborough California United States Centre for Imaging Sciences The University of Manchester Manchester United Kingdom Department of Epidemiology and Biostatistics University of California San Francisco California United States Department of Internal Medicine Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands

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Repurposing Drugs for Alzheimer's Diseases through Link Prediction on Biomedical Literature

Repurposing Drugs for Alzheimer's Diseases through Link Pred...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Yongkang Xiao Yu Hou Huixue Zhou Gayo Diallo Marcelo Fiszman Julian Wolfson Halil Kilicoglu You Chen Hua Xu William G. Mantyh Rui Zhang Institute for Health Informatics University of Minnesota Minneapolis MN USA Department of Surgery University of Minnesota Minneapolis MN USA INRIA SISTM Team AHeaD -INSERM 1219 Bordeaux Population Health University of Bordeaux France NITES - Núcleo de Inovação e Tecnologia Em Saúde Pontifical Catholic University of Rio de Janeiro Brazil Division of Biostatistics School of Public Health University of Minnesota Minneapolis MN USA School of Information Sciences University of Illinois Urbana-Champaign Champaign IL USA Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN USA Section of Biomedical Informatics and Data Science Yale University New Haven Connecticut USA Department of Neurology University of Minnesota Minneapolis MN USA

Recently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement (DS) domain knowledge graph, SuppKG, with semantic triples from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set, and was used to generate the score tables for the link prediction task. According to the results of link prediction, we proposed candidate drugs for AD. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover novel drugs for AD. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.

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On model-based time trend adjustments in platform trials with non-concurrent controls

arXiv

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arXiv 2021年

作者： Roig, Marta Bofill Krotka, Pavla Burman, Carl-Fredrik Glimm, Ekkehard Gold, Stefan M. Hees, Katharina Jacko, Peter Koenig, Franz Magirr, Dominic Mesenbrink, Peter Viele, Kert Posch, Martin Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Statistical Innovation Data Science & Artiffcial Intelligence AstraZeneca Gothenburg Sweden Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland Institute of Biometry and Medical Informatics University of Magdeburg Magdeburg Germany Klinik für Psychiatrie und Psychotherapie Charité - Universitätsmedizin Berlin Campus Benjamin Franklin Berlin Germany Medizinische Klinik m.S. Psychosomatik Charité - Universitätsmedizin Berlin Campus Benjamin Franklin Berlin Germany Zentrum für Molekulare Neurobiologie Universitätsklinikum Hamburg Eppendorf Hamburg Germany Section of Biostatistics Paul-Ehrlich-Institut Langen Germany Lancaster University Lancaster United Kingdom Analytics Global Drug Development Novartis Pharmaceuticals Corporation One Health Plaza East HanoverNJ United States

Background Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial's efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. Methods We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. Results A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a s

关键词： Additives

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Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

arXiv

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arXiv 2023年

作者： Zhou, Huixue Austin, Robin Lu, Sheng-Chieh Silverman, Greg Zhou, Yuqi Kilicoglu, Halil Xu, Hua Zhang, Rui Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Nursing University of Minnesota MinneapolisMN United States The University of Texas MD Anderson Cancer Center Department of Symptom Research HoustonTX United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States Department of Pharmaceutical Care & Health Systems University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois Urbana-ChampaignIL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven United States

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts. © 2023, CC BY-NC-SA.

关键词： Terminology

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Reports of the AAAI 2010 conference workshops

Reports of the AAAI 2010 conference workshops

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作者： Aha, David W. Boddy, Mark Bulitko, Vadim D'Avila Garcez, Artur S. Doshi, Prashant Edelkamp, Stefan Geib, Christopher Gmytrasiewicz, Piotr Goldman, Robert P. Halevy, Alon Hitzler, Pascal Isbell, Charles Josyula, Darsana Kaelbling, Leslie Pack Kersting, Kristian Kunda, Maithilee Lamb, Luis C. Marthi, Bhaskara McGreggor, Keith Mihalkova, Lilyana Nastase, Vivi Natarajan, Sriraam Provan, Gregory Raja, Anita Ram, Ashwin Riedl, Mark Russell, Stuart Sabharwal, Ashish Smaus, Jan-Georg Sukthankar, Gita Tuyls, Karl Van Der Meyden, Ron Adaptive Systems Section Naval Research Laboratory Washington DC United States Adventium Labs. Minneapolis MN United States Department of Computer Science University of Alberta Canada City University London United Kingdom University of Georgia United States University of Bremen Germany School of Informatics University of Edinburgh United Kingdom University of Illinois Chicago United States Smart Information Flow Technologies LLC Minneapolis MN United States Structured Data Group Google Research United States Department of Computer Science Wright State University Dayton OH United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science Bowie State University United States Department of Computer Science and Engineering Massachusetts Institute of Technology United States Statistical Relational Mining Group Fraunhofer IAIS Germany Department of Computer Science Federal University of Rio Grande Do sul Brazil Willow Garage Inc. CA United States Computer Science Department University of Maryland College Park United States Heidelberg Institute for Theoretical Studies Heidelberg Germany Department of Computer Science University of Wisconsin Madison United States University College Cork Ireland Cork Complex Systems Lab. Ireland Department of Software and Information Systems University of North Carolina Charlotte United States Department of Computer Science and Engineering University of California Berkeley United States Department of Computer Science Cornell University United States Institute for Computational Sustainability United States University of Freiburg Germany School of Electrical Engineering and Computer Science University of Central Florida United States Maastricht University Netherlands University of New South Wales NICTA Australia

The AAAI-10 workshop program was held on July 11-12, 2010, at the Westin Peachtree Plaza in Atlanta, Georgia. The workshop program included 13 workshops covering a wide range of topics in artificial intelligence. There were several presentations on particular formalisms for combining the levels of planning from the perspectives of classical planning, motion planning, POMDPs, search-based planning, and temporal logic-based constraints. Henry Lieberman from Massachusetts Institute of Technology Media Laboratory presented work developing a platform for knowledge acquisition and usage from expert users, built upon experience with eliciting information from nonexperts in a collaborative manner. The objective of the AAAI Workshop on Goal-Directed Autonomy (GDA) was to encourage discussion and novel contributions on intelligent agents that can self-select their goals. Jeremy Baxter from QinetiQ focused on user interaction, while Nick Hawes described a goal-directed reasoning framework for controlling a mobile robot.

关键词： Motion planning

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Geometric Structures Induced by Deformations of the Legendre Transform

arXiv

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arXiv 2022年

作者： Morales, Pablo A. Korbel, Jan Rosas, Fernando E. Research Division Araya Inc. Tokyo107-6019 Japan Section for Science of Complex Systems Center for Medical Data Science Medical University of Vienna Spitalgasse 23 Vienna1090 Austria Complexity Science Hub Vienna Josefstädter Strasse 39 Vienna1080 Austria Department of Informatics University of Sussex BrightonBN1 9RH United Kingdom Centre for Psychedelic Research Department of Brain Science Imperial College London LondonSW7 2DD United Kingdom Centre for Complexity Science Imperial College London LondonSW7 2AZ United Kingdom Centre for Eudaimonia and Human Flourishing University of Oxford OxfordOX3 9BX United Kingdom

The recent link discovered between generalized Legendre transforms and non-dually flat statistical manifolds suggests a fundamental reason behind the ubiquity of Rényi's divergence and entropy in a wide range of physical phenomena. However, these early findings still provide little intuition on the nature of this relationship and its implications for physical systems. Here we shed new light on the Legendre transform by revealing the consequences of its deformation via symplectic geometry and complexification. These findings reveal a novel common framework that leads to a principled and unified understanding of physical systems that are not well-described by classic information-theoretic quantities. Copyright © 2022, The Authors. All rights reserved.

关键词： Information theory

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Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

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Computer Methods and Programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

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The All of Us data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research

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Biomedical data science 1000年第1期6卷 443-464页

作者： Kelsey R. Mayo Melissa A. Basford Robert J. Carroll Moira Dillon Heather Fullen Jesse Leung Hiral Master Shimon Rura Lina Sulieman Nan Kennedy Eric Banks David Bernick Asmita Gauchan Lee Lichtenstein Brandy M. Mapes Kayla Marginean Steve L. Nyemba Andrea Ramirez Charissa Rotundo Keri Wolfe Weiyi Xia Romuladus E. Azuine Robert M. Cronin Joshua C. Denny Abel Kho Christopher Lunt Bradley Malin Karthik Natarajan Consuelo H. Wilkins Hua Xu George Hripcsak Dan M. Roden Anthony A. Philippakis David Glazer Paul A. Harris 1Vanderbilt Institute for Clinical and Translational Research Vanderbilt University Medical Center Nashville Tennessee USA 2Deparment of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA email: paul.a.harris@*** 3Verily Life Sciences South San Francisco California USA 4Data Sciences Platform Broad Institute of Harvard and MIT Cambridge Massachusetts USA 5Broad Institute of Harvard and MIT Cambridge Massachusetts USA 6The All of Us Research Program National Institutes of Health Bethesda Maryland USA 7Vanderbilt University Medical Center Enterprise Cybersecurity Vanderbilt University Medical Center Nashville Tennessee USA 8Department of Internal Medicine The Ohio State University Columbus Ohio USA 9Department of Medicine and Institute for Augmented Intelligence in Medicine Feinberg School of Medicine Northwestern University Chicago Illinois USA 10Department of Biomedical Informatics Columbia University New York NY USA 11Department of Medicine Vanderbilt University Medical Center Nashville Tennessee USA 12Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA 13Department of Pharmacology Vanderbilt University Medical Center Nashville Tennessee USA

The All of Us Research Program's data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

关键词： precision medicine data ecosystem diversity data privacy data integration electronic health records

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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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Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

arXiv

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arXiv 2023年

作者： Yang, Rui Zeng, Qingcheng You, Keen Qiao, Yujie Huang, Lucas Hsieh, Chia-Chun Rosand, Benjamin Goldwasser, Jeremy Dave, Amisha D. Keenan, Tiarnan D.L. Chew, Emily Y. Radev, Dragomir Lu, Zhiyong Xu, Hua Chen, Qingyu Li, Irene Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Linguistics Northwestern University EvanstonIL United States Department of Computer Science Yale University New HavenCT United States Yale School of Public Health Yale University New HavenCT United States Yale New Haven Hospital Yale School of Medicine Yale University New HavenCT United States Division of Epidemiology and Clinical Applications National Eye Institute National Institutes of Health BethesdaMD United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD United States Section of Biomedical Informatics and Data Science Yale School of Medicine Yale University New HavenCT United States Information Technology Center University of Tokyo Tokyo Japan

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. © 2023, CC0.

关键词： Machine learning

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