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Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations

arXiv

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arXiv 2024年

作者： Mu, Xiaofan Seyedi, Salman Zheng, Iris Jiang, Zifan Chen, Liu Omofojoye, Bolaji Hershenberg, Rachel Levey, Allan I. Clifford, Gari D. Dodge, Hiroko H. Kwon, Hyeokhyen Department of Computer Science Emory University AtlantaGA30322 United States Department of Biomedical Informatics Emory University AtlantaGA30322 United States Department of Biomedical Engineering Emory University Georgia Institute of Technology AtlantaGA30332 United States Department of Psychiatry and Behavioral Sciences Emory University AtlantaGA30322 United States Department of Neurology Emory University AtlantaGA30322 United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 individuals with normal cognition or Mild Cognitive Impairment derived from remote video conversations and classified cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.78 area under the receiver operating characteristic curve (AUC), social isolation with 0.75 AUC, neuroticism with 0.71 AUC, and negative affect scales with 0.79 AUC. Recent advances in machine learning offer new opportunities to remotely detect cognitive impairment and assess associated factors, such as neuroticism and psychological well-being. Our experiment showed that speech and language patterns were more useful for quantifying cognitive impairment, whereas facial expression and cardiovascular patterns using photoplethysmography (PPG) were more useful for quantifying personality and psychological well-being. © 2024, CC BY-NC-SA.

关键词： Photoplethysmography

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Assessing patient similarity through representation learning on medical records

arXiv

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arXiv 2021年

作者： Memarzadeh, Hoda Ghadiri, Nasser Samwald, Matthias Shahreza, Maryam Lotfi Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Department of Computer Engineering University of Isfahan Shahreza Campus Iran

MSC Codes 68P20Patient similarity assessments, which identify patients similar to a given patient, can help improve medical care. They can be performed using electronic medical records (EMRs). This makes it necessary to convert heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods were developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that accounts for information in clinical narratives. To address the limitations of previous approaches, we propose an unsupervised method for building a patient representation that utilizes unstructured data integrated with structured data extracted from patients' EMR. For modeling extracted data, we employ a tree structure that captures the temporal relations of multiple medical events from EMR. We process clinical notes to extract symptoms, signs, and diseases using different tools such as Medspacy, MetaMap, and Scispacy and map entities to UMLS. After creating a tree data structure, we utilize two novel relabeling methods for the non-leaf nodes of the tree to capture two kinds of temporal aspects in extracted events. By traversing the tree, we generate a sequence that can be used to create an embedding vector for each patient. We extensively evaluate the proposed method across patient similarity and prediction tasks, and demonstrate that our methodology leads to lower mean squared error (MSE) and higher precisions and normalized discounted cumulative gain (NDCG) relative to baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical computing

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On the robustness of EEG tensor completion methods

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Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

引用

International Conference on engineering technology and their Applications (IICETA)

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Isam Abu-Qasmieh Ali Mohammad Alqudah Ahmed Alkhayyat King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Electrical and Computer Engineering Biomedical Engineering Program University of Manitoba Canada Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images.

关键词： Image segmentation Visualization Transfer learning Feature extraction Convolutional neural networks Artificial intelligence X-ray imaging

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Hidden Markov Models for Sentiment Analysis in Social Media 4

Hidden Markov Models for Sentiment Analysis in Social Media

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4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019

作者： Perikos, Isidoros Kardakis, Spyridon Paraskevas, Michael Hatzilygeroudis, Ioannis Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press diophantus Patras Greece Department of Electrical and Computer Engineering University of Peloponnese and Computer Technology Institute and Press diophantus Patras Greece

ISBN: (纸本)9781728108865

User-generated content in social media contain very meaningful information about public sentiment and stance and require automated methods to analyze them extract knowledge from them. The recognition of sentiments, emotions and attitudes in textual data is necessary for understanding public stance and is highly desired for various services and procedures. In this work, we examine the performance of Hidden Markov Models (HMM) in the recognition of sentiments and opinions in text. Hidden Markov Models constitute a quite suitable approach given the special characteristics of the textual data. In particular, they can utilize the sequential nature of textual data, a piece of information that traditional machine learning approaches fail to fully take into account. We performed several experiments in order to assess the performance of different HMM-based methods under various training parameters and architectures. The evaluation results indicate that Hidden Markov Models achieve superior performance compared to traditional machine learning algorithms and highlight that they are scalable and accurate in analyzing user generated content and in specifying opinions and attitudes. © 2019 IEEE.

关键词： Hidden Markov models

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Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

arXiv

引用

arXiv 2022年

作者： Zhou, Wei Zhang, Ruizeng Li, Leida Liu, Hantao Chen, Huiyan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Beijing Institute of Technology Beijing100081 China School of Artificial Intelligence Xidian University Xi’an710071 China School of Computer Science and Informatics Cardiff University CardiffCF24 4AX United Kingdom

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms. © 2022, CC BY.

关键词： Image quality

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Challenges and working solutions in agile adaptation: Experiences from the industry 30

Challenges and working solutions in agile adaptation: Experi...

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Joint 30th International Workshop on Software Measurement and the 15th International Conference on Software Process and Product Measurement, IWSM-Mensura 2020

作者： Özcan-Top, Özden Demirors, Onur McCaffery, Fergal Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Computer Engineering Izmir Institute of Technology Izmir Turkey Regulated Software Research Centre Dundalk Institute of Technology Dundalk Ireland STATSports Group Newry Ireland

Challenges in agile adaptation is inevitable in software development projects and have to be dealt with by software practitioners. The pathway to excellence in agility requires experience of challenges, failure of process scenarios;and the discovery of working solutions by software development teams. The major purpose of this study is to highlight both the challenges organizations faced when implementing agile techniques and the solutions adopted that proved successful. In order to specify these challenges and working solutions, we performed a multiple case study by using the Software Agility Assessment Reference Model (AgilityMod). In this paper, we describe two cases that achieve the highest levels of agility among eight cases and describe their experiences in achieving a good adaptation through the challenges that they faced and the solutions that were found for these challenges. Additionally, we provide two challenges that have not been resolved yet and are subject to further discussions. Copyright © 2020 for this paper by its authors.

关键词： Software design

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Roadmap on Neuromorphic Photonics

arXiv

引用

arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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