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arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical AI ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. © 2024, CC BY.

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HRnV-Calc: A software package for heart rate n-variability and heart rate variability analysis

arXiv

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arXiv 2021年

作者： Niu, Chenglin Guo, Dagang Hock Ong, Marcus Eng Koh, Zhi Xiong Wah Ho, Andrew Fu Lin, Zhiping Liu, Chengyu Clifford, Gari D. Liu, Nan Duke-NUS Medical School National University of Singapore Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Instrument Science and Engineering Southeast University Nanjing China Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments made in HRV metric research over the last few decades, the representation of the heartbeat sequence upon which HRV is based has received relatively little attention. The recently introduced heart rate n-variability (HRnV) offers an alternative to R-to-R peak interval representations which complements conventional HRV analysis by considering HRV behavior on varying scales. Although HRnV has been shown to improve triage in pilot studies, there is currently no open and standard software to support future research of HRnV and its broader clinical applications. We aimed to develop an open, reliable, and easy to use software package implementing HRnV for further research and improvements of HRnV. This package has been designed to facilitate collaborative investigations between clinicians and researchers to study HRnV in various contexts and applications. Approach: We developed an open-source software, HRnV-Calc, based on the PhysioNet Cardiovascular Signal Toolbox (PCST), which features comprehensive graphical user interfaces (GUIs) for HRnV and HRV analysis. Main results: While preserving the core functionalities and performance of PCST, HRnV-Calc enables step-by-step manual inspection and configuration of HRV and HRnV analysis, so that results can be debugged, easily interpreted, and integrated to downstream applications. Significance: The open-source HRnV-Calc software, an accessible and standardized HRV and HRnV analysis platform, enhances the scope of HRV assessment and is designed to assist in future improvements and applications of HRnV and related research. © 2021, CC BY.

关键词： Heart

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Wait, but Tylenol is Acetaminophen… Investigating and Improving Language Models' Ability to Resist Requests for Misinformation

arXiv

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arXiv 2024年

作者： Chen Gao, Mingye Sasse, Kuleen Hartvigsen, Tom Anthony, Brian Fan, Lizhou Aerts, Hugo Gallifant, Jack Bitterman, Danielle Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women’s Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States Massachusetts Institute of Technology CambridgeMA United States Johns Hopkins University BaltimoreMD United States Department of Data Science University of Virginia CharlottesvilleVA United States Radiology and Nuclear Medicine GROW & CARIM Maastricht University Netherlands

Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information. In medicine, this could accelerate the generation of misinformation that impacts human well-being. Objectives/Methods: We analyzed compliance to requests to generate misleading content about medications in settings where models know the request is illogical. We investigated whether in-context directions and instruction-tuning of LLMs to prioritize logical reasoning over compliance reduced misinformation risk. Results: While all frontier LLMs complied with misinformation requests, both prompt-based and parameter-based approaches can improve the detection of logic flaws in requests and prevent the dissemination of medical misinformation. Conclusion: Shifting LLMs to prioritize logic over compliance could reduce risks of exploitation for medical misinformation. © 2024, CC BY-NC-ND.

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Multimodal learning with graphs

arXiv

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arXiv 2022年

作者： Ektefaie, Yasha Dasoulas, George Noori, Ayush Farhat, Maha Zitnik, Marinka Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Department of Biomedical Informatics Harvard University BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Division of Pulmonary and Critical Care Department of Medicine Massachusetts General Hospital BostonMA United States

Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases—the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models. © 2022, CC BY.

关键词： Blueprints

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Towards a Visual-Language Foundation Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

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Graph Artificial Intelligence in Medicine

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Biomedical data science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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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 relevance of research data sharing and reuse studies

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Bulletin of the Association for Information science and Technology 2013年第6期39卷

作者： Nicholas M. Weber PhD Student University of Illinois where he is affiliated with the Center for Informatics Research in Science and Scholarship. Weber is also a fellow in the Data Curation Education in Research Centers (DCERC) program at the National Center for Atmospheric Research. He works on projects related to data curation and software studies

Editor's Summary As big data takes the limelight, it is critical that the value of research data is appreciated and the field of information science garners respect for its contributions. Despite being a logical leader in data management and curation, information science competes with other disciplines in handling research data and often fails to demonstrate its own expertise. Two developments presented through the annual Research data Access & Preservation (RDAP) Summits hold promise for promoting the field and demonstrating its relevance to data management topics. First, the use of formal and systematic planning for data management is expanding, driven by requirements of many funding agencies. Second, research is offering insights into how researchers appraise, share and reuse data, with identification of data quality indicators related to satisfaction. As the effective use of research data becomes more pressing, the field of information science should lead in demonstrating the value derived from professional data curation, and the RDAP community must define and articulate its own research agenda.

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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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