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machine learning to detect recent recreational drug use in intensive cardiac care units

Archives of Cardiovascular Diseases 2025年第5期118卷 277-286页

作者： El Bèze, Nathan Hamzi, Kenza Henry, Patrick Trimaille, Antonin El Ouahidi, Amine Zakine, Cyril Nallet, Olivier Delmas, Clément Aboyans, Victor Goralski, Marc Albert, Franck Bonnefoy-Cudraz, Eric Bochaton, Thomas Schurtz, Guillaume Lim, Pascal Lequipar, Antoine Gonçalves, Trecy Gall, Emmanuel Pommier, Thibaut Lemarchand, Léo Meune, Christophe Azzakani, Sonia Bouleti, Claire Amar, Jonas Dillinger, Jean-Guillaume Steg, P. Gabriel Vicaut, Eric Toupin, Solenn Pezel, Théo Inserm MASCOT – UMRS 942 Department of Cardiology University Hospital of Lariboisière Université Paris-Cité AP–HP Paris 75010 France Multimodality Imaging Research for Analysis Core Laboratory: Artificial Intelligence (MIRACL.ai) Department of Data Science Machine Learning and Artificial Intelligence in Health University Hospital of Lariboisière AP–HP Paris 75010 France Department of Cardiovascular Medicine Nouvel Hôpital Civil Strasbourg University Hospital Strasbourg 67000 France Department of Cardiology University Hospital of Brest Brest 29609 France NCT+ Saint-Cyr-sur-Loire 37540 France Department of Cardiology Hôpital Montfermeil Montfermeil 93370 France Department of Cardiology Rangueil University Hospital Toulouse 31000 France Department of Cardiology University Hospital of Limoges Limoges 87000 France Department of Cardiology Centre Hospitalier d'Orléans Orléans 45100 France Department of Cardiology Centre Hospitalier de Chartres Le Coudray 28630 France Intensive Cardiological Care Division Louis-Pradel Hospital Hospices Civils de Lyon Bron 69500 France Department of Cardiology University Hospital of Lille Lille 59000 France Intensive Cardiac Care Unit Henri-Mondor University Hospital Créteil 94000 France Department of Cardiology Dijon University Hospital Dijon 21000 France Department of Cardiology and Vascular Diseases CHU of Rennes Rennes 35000 France Department of Cardiology Hôpital Avicenne AP–HP Bobigny 93000 France Department of Cardiology Clinical Investigation Centre (Inserm 1204) University Hospital of Poitiers Poitiers 86000 France Inserm_U1148/LVTS hôpital Bichat université Paris-Cité AP–HP Paris 75877 France Unité de recherche clinique hôpital Fernand-Widal AP–HP Paris 75010 France

Background: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this issue, machine learning methods could assist in the detection of recreational drug use. Aims: To investigate the accuracy of a machine learning model using clinical, biological and echocardiographic data for detecting recreational drug use in patients admitted to intensive cardiac care units. Methods: From 07 to 22 April 2021, systematic screening for all traditional recreational drugs (cannabis, opioids, cocaine, amphetamines, 3,4-methylenedioxymethamphetamine) was performed by urinary testing in all consecutive patients admitted to intensive cardiac care units in 39 French centres. The primary outcome was recreational drug detection by urinary testing. The framework involved automated variable selection by eXtreme Gradient Boosting (XGBoost) and model building with multiple algorithms, using 31 centres as the derivation cohort and eight other centres as the validation cohort. Results: Among the 1499 patients undergoing urinary testing for drugs (mean age 63 ± 15 years;70% male), 161 (11%) tested positive (cannabis: 9.1%;opioids: 2.1%;cocaine: 1.7%;amphetamines: 0.7%;3,4-methylenedioxymethamphetamine: 0.6%). Of these, only 57% had reported drug use. Using nine variables, the best machine learning model (random forest) showed good performance in the derivation cohort (area under the receiver operating characteristic curve = 0.82) and in the validation cohort (area under the receiver operating characteristic curve = 0.76). Conclusions: In a large intensive cardiac care unit cohort, a comprehensive machine learning model exhibited good performance in detecting recreational drug use, and provided valuable insights into the relationships between clinical variables and drug use through explainable machine learning techniques. ©

关键词： Intensive cardiac care machine learning Recreational drug use

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Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers

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Heliyon 2025年第2期11卷 e41656页

作者： Milling, Manuel Rampp, Simon D.N. Triantafyllopoulos, Andreas Plaza, Maria P. Brunner, Jens O. Traidl-Hoffmann, Claudia Schuller, Björn W. Damialis, Athanasios CHI – Chair of Health Informatics MRI Technical University of Munich Munich Germany MCML–Munich Center for Machine Learning Germany EIHW – Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Augsburg Germany Institute of Environmental Medicine and Integrative Health Faculty of Medicine University Clinic of Augsburg & University of Augsburg Augsburg Germany Institute of Environmental Medicine Helmholtz Center Munich German Research Center for Environmental Health Germany Faculty of Business and Economics and Faculty of Medicine University of Augsburg Augsburg Germany Department of Technology Management and Economics Technical University of Denmark Denmark Next Generation Technology Region Zealand Denmark Christine Kühne Center for Allergy Research and Education Davos Switzerland MDSI–Munich Data Science Institute Germany GLAM–the Group on Language Audio & Music Imperial College London London United Kingdom Terrestrial Ecology and Climate Change Department of Ecology School of Biology Faculty of Sciences Aristotle University of Thessaloniki Thessaloniki Greece

Deep-learning-based classification of pollen grains has been a major driver towards automatic monitoring of airborne pollen. Yet, despite an abundance of available datasets, little effort has been spent to investigate which aspects pose the biggest challenges to the (often black-box- resembling) pollen classification approaches. To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms. It is here concluded that the main challenges lie in A) the (partly) co-occurring of multiple pollen grains in a single image, B) the occlusion of specific markers through the 2D capturing of microscopy images, and C) for some taxa, a general lack of salient, unique features. Our code is publicly available under https://***/millinma/SDPollen © 2025

关键词： Deep learning Pollen recognition Sample difficulty analysis

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Explaining bayesian neural networks

arXiv

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arXiv 2021年

作者： Bykov, Kirill Höhne, Marina M.-C. Creosteanu, Adelaida Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Marchstr. 23 Berlin10587 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg 4 Saarbrücken66123 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Technische Universität Berlin Berlin Germany Google Research Brain team Berlin Germany Department of Computer Science TU Kaiserslautern Germany Heidelberg Germany Institute of Pathology Charite – Universitätsmedizin Berlin Berlin Germany Aignostics Berlin Germany RIKEN AIP 1-4-1 Nihonbashi Chuo-ku Tokyo Japan

—To make advanced learning machines such as Deep Neural Networks (DNNs) more transparent in decision making, explainable AI (XAI) aims to provide interpretations of DNNs’ predictions. These interpretations are usually given in the form of heatmaps, each one illustrating relevant patterns regarding the prediction for a given instance. Bayesian approaches such as Bayesian Neural Networks (BNNs) so far have a limited form of transparency (model transparency) already built-in through their prior weight distribution, but notably, they lack explanations of their predictions for given instances. In this work, we bring together these two perspectives of transparency into a holistic explanation framework for explaining BNNs. Within the Bayesian framework, the network weights follow a probability distribution. Hence, the standard (deterministic) prediction strategy of DNNs extends in BNNs to a predictive distribution, and thus the standard explanation extends to an explanation distribution. Exploiting this view, we uncover that BNNs implicitly employ multiple heterogeneous prediction strategies. While some of these are inherited from standard DNNs, others are revealed to us by considering the inherent uncertainty in BNNs. Our quantitative and qualitative experiments on toy/benchmark data and real-world data from pathology show that the proposed approach of explaining BNNs can lead to more effective and insightful explanations. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Sharpening the dark matter signature in gravitational waveforms. II. Numerical simulations

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Physical Review D 2025年第6期111卷 063071-063071页

作者： Bradley J. Kavanagh Theophanes K. Karydas Gianfranco Bertone Pierfrancesco Di Cintio Mario Pasquato Instituto de Física de Cantabria (IFCA UC-CSIC) Avenue de Los Castros s 39005 Santander Spain Gravitation Astroparticle Physics Amsterdam (GRAPPA) Institute for Theoretical Physics Amsterdam and Delta Institute for Theoretical Physics University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands Consiglio Nazionale delle Ricerche Istituto dei Sistemi Complessi (CNR-ISC) via Madonna del Piano 17 50022 Sesto Fiorentino (FI) Italy INAF-Osservatorio Astronomico di Arcetri Largo Enrico Fermi 5 50125 Firenze Italy INFN-Sezione di Firenze Via Giovanni Sansone 1 50022 Sesto Fiorentino Italy Département de Physique Université de Montréal 1375 Avenue Thérèse-Lavoie-Roux Montréal Canada Mila—Quebec Artificial Intelligence Institute 6666 Rue Saint-Urbain Montréal Canada Ciela—Montréal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 5 Padova Italy Istituto Nazionale di Fisica Nucleare Padova Via Marzolo 8 Padova Italy

Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modeling of the dynamical friction, induced by dark matter on the secondary compact object in intermediate and extreme mass ratio inspirals. In this paper, we introduce nbodyimri, a new publicly available code designed for simulating binary systems within cold dark matter spikes. Leveraging higher particle counts and finer time steps, we validate the applicability of the standard dynamical friction formalism and provide an accurate determination of the maximum impact parameter of particles which can effectively scatter with a compact object, across various mass ratios. We also show that in addition to feedback due to dynamical friction, the dark matter also evolves through a stirring effect driven by the time-dependent potential of the binary. We introduce a simple semianalytical scheme to account for this effect and demonstrate that including stirring tends to slow the rate of dark matter depletion and therefore enhances the impact of dark matter on the dynamics of the binary.

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A study on the uncertainty of convolutional layers in deep neural networks

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University ShenzhenGuangdong518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution. © 2020, CC-BY.

关键词： Convolution

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Synchronized sensor insoles for clinical gait analysis in home-monitoring applications

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Current Directions in Biomedical Engineering 2018年第1期4卷 433-437页

作者： Roth, Nils Martindale, Christine F. Gaßner, Heiko Kohl, Zacharias Klucken, Jochen Eskofer, Bjoern M. Machine Learning and Data Analytics Lab. Department of Computer Science Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Erlangen Germany Department of Molecular Neurology University Hospital Erlangen Germany

Wearable sensor systems are of increasing interest in clinical gait analysis. However, little information about gait dynamics of patients under free living conditions is available, due to the challenges of integrating such systems unobtrusively into a patient's everyday live. To address this limitation, new, fully integrated low power sensor insoles are proposed, to target applications particularly in home-monitoring scenarios. The insoles combine inertial as well as pressure sensors and feature wireless synchronization to acquire biomechanical data of both feet with a mean timing offset of 15.0 μs. The proposed system was evaluated on 15 patients with mild to severe gait disorders against the GAITRite® system as reference. Gait events based on the insoles' pressure sensors were manually extracted to calculate temporal gait features such as double support time and double support. Compared to the reference system a mean error of 0.06 s ± 0.06 s and 3.89 % ± 2.61 % was achieved, respectively. The proposed insoles proved their ability to acquire synchronized gait parameters and address the requirements for home-monitoring scenarios, pushing the boundaries of clinical gait analysis. © 2018 Nils Roth et al.

关键词： Synchronization

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Incorporating Hidden Layer representation into Adversarial Attacks and Defences

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adversarial perturbation. And this defence strategy can be regarded as an activation function which can be applied any kind of neural networks. We also prove theoretically the effectiveness of this defense strategy under certain conditions. Besides, incorporating hidden layer representation we propose three types of adversarial attacks to generate three types of adversarial examples, respectively. The experiments show that our defence method can significantly improve the adversarial robustness of deep neural networks which achieves the state-of-the-art performance even though we do not adopt adversarial training. © 2020, CC BY.

关键词： Deep neural networks

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Reducing Training data Needs with Minimal Multilevel machine learning (M3L)

arXiv

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arXiv 2023年

作者： Heinen, Stefan Khan, Danish von Rudorff, Guido Falk Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Price, Alastair J.A. Nandi, Surajit Bhowmik, Arghya Hermansson, Kersti Anatole von Lilienfeld, O. Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry University of Toronto St. George Campus TorontoON Canada University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Departments of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Energy Conversion and Storage DTU Anker Engelunds Vej Kgs. LyngbyDK-2800 Denmark Department of Materials Science and Engineering University of Toronto St. George campus TorontoON Canada Department of Physics University of Toronto St. George campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin Germany

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating tens with basis sets TZ, cc-pVTZ, and AVTZF12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼ 1.01, 1.1, 3.8, 13.8 and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9LCCSD(T), EGP, QM9CCSD(T)AE , and QM9CCSD(T)EA , respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP∗(VWN5), TPSSH. © 2023, CC0.

关键词： Carbon footprint

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Ranking-based convolutional neural network models for peptide-MHC binding prediction

arXiv

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arXiv 2020年

作者： Chen, Ziqi Min, Martin Renqiang Ning, Xia Computer Science and Engineering Department Ohio State University ColumbusOH United States Machine Learning Department NEC Labs America PrincetonNJ United States Biomedical Informatics Department Ohio State University ColumbusOH United States Translational Data Analytics Institute Ohio State University ColumbusOH United States

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network (CNN)based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles. © 2020, CC BY.

关键词： Peptides

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Trend filtering - II. Denoising astronomical signals with varying degrees of smoothness

arXiv

引用

arXiv 2020年

作者： Politsch, Collin A. Cisewski-Kehe, Jessi Croft, Rupert A.C. Wasserman, Larry Department of Statistics & Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States McWilliams Center for Cosmology Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Yale University New HavenCT06520 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States School of Physics University of Melbourne VIC3010 Australia

Trend filtering-first introduced into the astronomical literature in Paper I of this series-is a state-of-the-art statistical tool for denoising one-dimensional signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-α forest of quasar spectra;(2) more general spectroscopy of quasars, galaxies, and stars;(3) stellar light curves with planetary transits;(4) eclipsing binary light curves;and (5) supernova light curves. We study the Lyman-α forest in the greatest detail-using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra;and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for one-dimensional data reduction and compression. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

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