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Guidance of development,validation,and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

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Military Medical Research 2025年第5期 788-798页

作者： Wen Wang Ying-Hui Jin Mei Liu Qiao He Jia-Yue Xu Ming-Qi Wang Guo-Wei Li Bo Fu Si-Yu Yan Kang Zou Xin Sun Institute of Integrated Traditional Chinese and Western Medicine Chinese Evidence-Based Medicine and Cochrane China CenterWest China HospitalSichuan University NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan Sichuan Center of Technology Innovation for Real World Data Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan University Department of Health Research Methods Evidence and ImpactMcMaster University Center for Clinical Epidemiology and Methodology Guangdong Second Provincial General Hospital Biostatistics Unit Research Institute at St.Joseph's Healthcare Hamilton School of Computer Science and Engineering University of Electronic Science and Technology of China West China School of Public Health and West China Fourth Hospital Sichuan University

Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD).These studies rely on algorithms to identify specific health conditions (e.g.,diabetes or sepsis) for statistical ***,there has been substantial variation in the algorithm development and validation,leading to frequently suboptimal performance and posing a significant threat to the validity of study ***,these issues are often ***:We systematically developed guidance for the development,validation,and evaluation of algorithms designed to identify health status (DEVELOP-RCD).Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development,validation,and ***,we conducted an empirical study on an algorithm for identifying *** on these findings,we formulated specific workflow and recommendations for algorithm development,validation,and evaluation within the ***,the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize ***:A standardized workflow for algorithm development,validation,and evaluation was *** by specific health status considerations,the workflow comprises four integrated steps:assessing an existing algorithm’s suitability for the target health status;developing a new algorithm using recommended methods;validating the algorithm using prescribed performance measures;and evaluating the impact of the algorithm on study ***,13 good practice recommendations were formulated with detailed ***,a practical study on sepsis identification was included to demonstrate the application of this ***:The establishment of guidance is intended to aid researchers and clinician

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Voxel-Wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification Using Deep Multimodal Learning

SSRN

引用

SSRN 2025年

作者： Itkyal, Vaibhavi Sanjeet Abrol, Anees LaGrow, Theodore J. Fedorov, Alex Calhoun, Vince D. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University GA United States Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University GA United States Department of Neuroscience Emory University GA United States Emory University United States

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis to support clinical decision-making and future intervention strategies. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI (sMRI) are promising tools for identifying AD-related brain alterations. In this study, we propose a novel deep learning framework that fuses spatial maps of rs-fMRI functional networks—via a new voxelwise intensity projection (iVIP) method—and gray matter segmentations to classify subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our multi-channel 3D convolutional neural network (CNN), inspired by the AlexNet architecture, integrates anatomical and functional modalities to perform both two-way (AD vs. CN) and three-way (AD vs. MCI vs. CN) classification tasks. While the two-way classification achieves high performance (94.12% accuracy, 97.79 AUC), the three-way classification offers clinically relevant utility, achieving 62.68% accuracy and 68.31 AUC, and enables finer differentiation among diagnostic subtypes. Comparative analysis shows that our iVIP representation outperforms traditional rs-fMRI measures such as ALFF and fALFF, and the fusion of iVIP with sMRI consistently outperforms unimodal models. Saliency map visualizations further reveal discriminative regions—including the hippocampus, amygdala, caudate nucleus, and thalamus—aligned with AD pathology. Our findings highlight the potential of deep multimodal fusion to enhance early diagnosis and subtype differentiation in Alzheimer’s disease. © 2025, The Authors. All rights reserved.

关键词： Brain mapping

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On Aggregation in Ensembles of Multilabel Classifiers

arXiv

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arXiv 2020年

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Mencía, Eneldo Loza Fürnkranz, Johannes Heinz Nixdorf Institute Department of Computer Science Paderborn University Germany Knowledge Engineering Group TU Darmstadt Germany Computational Data Analytics Group JKU Linz Austria

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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Seeing biodiversity: Perspectives in machine learning for wildlife conservation

arXiv

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arXiv 2021年

作者： Tuia, Devis Kellenberger, Benjamin Beery, Sara Costelloe, Blair R. Zuffi, Silvia Risse, Benjamin Mathis, Alexander Mathis, Mackenzie Weygandt van Langevelde, Frank Burghardt, Tilo Kays, Roland Klinck, Holger Wikelski, Martin Couzin, Iain D. van Horn, Grant Crofoot, Margaret C. Stewart, Charles V. Berger-Wolf, Tanya Switzerland United States Max Planck Institute of Animal Behavior Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Department of Biology University of Konstanz Germany Institute for Applied Mathematics and Information Technologies IMATI-CNR Italy Computer Science Department University of Münster Germany Switzerland Environmental Sciences Group Wageningen University Netherlands Computer Science Department University of Bristol United Kingdom Department of Forestry and Environmental Resources North Carolina State University United States North Carolina Museum of Natural Sciences United States Cornell Lab of Ornithology Cornell University United States Department of Computer Science Rensselaer Polytechnic Institute United States Translational Data Analytics Institute Ohio State University United States

data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation. © 2021, CC BY-NC-SA.

关键词： Ecology

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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M2: Mixed models with preferences, popularities and transitions for next-basket recommendation

arXiv

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arXiv 2020年

作者： Peng, Bo Ren, Zhiyun Parthasarathy, Srinivasan Ning, Xia Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States Department of Biomedical Informatics The Ohio State University ColumbusOH43210 United States

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for the evaluation of next-basket recommendation methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy

arXiv

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arXiv 2022年

作者： Bergmeir, Christoph de Nijs, Frits Sriramulu, Abishek Abolghasemi, Mahdi Bean, Richard Betts, John Bui, Quang Dinh, Nam Trong Einecke, Nils Esmaeilbeigi, Rasul Ferraro, Scott Galketiya, Priya Genov, Evgenii Glasgow, Robert Godahewa, Rakshitha Kang, Yanfei Limmer, Steffen Magdalena, Luis Montero-Manso, Pablo Peralta, Daniel Kumar, Yogesh Pipada Sunil Rosales-Pérez, Alejandro Ruddick, Julian Stratigakos, Akylas Stuckey, Peter Tack, Guido Triguero, Isaac Yuan, Rui Department of Data Science and Artificial Intelligence Monash University Melbourne Australia School of Mathematics and Physics University of Queensland Brisbane Australia Centre for Energy Data Innovation School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia School of Electrical and Electronics Engineering University of Adelaide Adelaide Australia Honda Research Institute Europe GmbH Offenbach am Main63073 Germany School of Information Technology Deakin University Melbourne Australia Building and Property Division Monash University Melbourne Australia EVERGi MOBI Vrije Universiteit Brussel Brussels Belgium School of Economics and Management Beihang University Beijing China E.T.S. Ingenieros Informáticos Universidad Politécnica de Madrid Madrid28660 Spain Disciple of Business Analytics University of Sydney Australia IDLab Department of Information Technology Ghent University - imec Belgium Department of Computer Science Centro de Investigación en Matem áticas Monterrey66629 Mexico Mines Paris PSL University Sophia Antipolis06904 France DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the "IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

引用

Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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