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Machine learning-based prediction of low-value care for hospitalized patients

Intelligence-Based Medicine 2023年 8卷 100115-100115页

作者： King, Andrew J. Tang, Lu Davis, Billie S. Preum, Sarah M. Bukowski, Leigh A. Zimmerman, John Kahn, Jeremy M. Department of Critical Care Medicine University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA United States Department of Computer Science Dartmouth College Hanover NH United States Human-Computer Interaction Institute Carnegie Mellon University School of Computer Science Pittsburgh PA United States

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period;and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system. © 2023 The Authors

关键词： Clinical decision support Evidence-based medicine Low-value care Machine learning Resuscitation

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ON ACCELERATING EDGE AI: OPTIMIZING RESOURCE-CONSTRAINED ENVIRONMENTS

arXiv

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arXiv 2025年

作者： Sander, Jacob Cohen, Achraf Dasari, Venkat R. Venable, Brent Jalaian, Brian Department of Computer Science University of West Florida PensacolaFL United States Department of Mathematics and Statistics University of West Florida PensacolaFL United States DEVCOM Army Research Laboratory Aberdeen Proving Ground MD United States Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States Department of Computer Science Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge. © 2025, CC BY.

关键词： Budget control

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research

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Biomedicine and Pharmacotherapy 2024年 181卷 117702-117702页

作者： Anwar, Faheem Zhang, Kuo Sun, Changcheng Pang, Meijun Zhou, Wanqi Li, Haodong He, Runnan Liu, Xiuyun Ming, Dong Medical School Tianjin University Tianjin 300072 China School of Pharmaceutical Science and Technology Tianjin University 300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300380 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin 300072 China

Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus. © 2024 The Authors

关键词： Clinical trials Hydrocephalus Non-surgical treatment Pathophysiology Pharmacological treatment Surgical interventions

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A 'plug and play' approach to testing virtual environment interaction techniques 6

A 'plug and play' approach to testing virtual environment in...

引用

6th Eurographics Workshop on Virtual Environments, EGVE 2000

作者： Willans, James S. Harrison, Michael D. Human-Computer Interaction Group Department of Computer Science University of York Heslington YorkYO10 5DD United Kingdom

ISBN: (纸本)3211835164

It is generally agreed that the usability of virtual environment interaction techniques is very poor. One reason for this is because toolkits used by virtual environment developers supply a small number of predefined techniques which are expected to be used regardless of context. In addition, there is no software to facilitate the design and testing of interaction techniques akin to that found for the appearance of the environment. We have developed the Marigold toolset to aid in the systematic design, testing and refining of virtual environment interaction techniques. The toolset uses a visual hybrid specification as a starting point. In this paper we demonstrate how Marigold can be used to aid in determining the suitability of interaction techniques by the rapid testing of alternatives in a `plug and play' style. © 2000 Proceedings of the 6th Eurographics Workshop on Virtual Environments, EGVE 2000. All rights reserved.

关键词： Virtual reality

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Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media

arXiv

引用

arXiv 2021年

作者： Wu, Julia Sivaraman, Venkatesh Kumar, Dheekshita Banda, Juan M. Sontag, David Dept of Electrical Engineering and Computer Science Massachusetts Institute of Technology Human-Computer Interaction Institute Carnegie Mellon University Department of Computer Science Georgia State University

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research. © 2021, CC BY.

关键词： Data mining

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

arXiv

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arXiv 2024年

作者： Vedove, Matteo Dalle Lamon, Edoardo Fontanelli, Daniele Palopoli, Luigi Saveriano, Matteo Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator. © 2024, CC BY-SA.

关键词： Intelligent robots

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Somatic Mutations and Alzheimer's Disease

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JOURNAL OF ALZHEIMERS DISEASE 2022年第2期90卷 475-493页

作者： [Anonymous] Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong ChinaDepartment of Computer Science and Technology University of Cambridge UK The Elton Laboratory for Molecular Neuroendocrinology Department of Human Molecular Genetics and Biochemistry Sackler Faculty of Medicine Adams Super Center for Brain Studies and Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel

Alzheimer's disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-beta plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer's disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerations, with the aim to bring hope for the vulnerable aging population.

关键词： Alzheimer's disease amyloid-beta peptide artificial intelligence big data late onset Alzheimer's disease somatic mutations tau

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Development of a compact mouse retinal imaging system using spectral domain optical coherence tomography (SD-OCT) with a full field stimulation channel for recording fast optoretinograms (ORGs) 34

Development of a compact mouse retinal imaging system using ...

引用

Ophthalmic Technologies XXXIV 2024

作者： Jo, Hang Chan Pijewska, Ewelina Meleppat, Ratheesh K. Jonnal, Ravi S. Kim, Dae Yu Zawadzki, Robert J. Department of Electrical and Computer Engineering Inha University 100 Inha-ro Michuhol-gu Incheon22212 Korea Republic of Dept. of Ophthalmology and Vision Science UC Davis Eye Center SacramentoCA95817 United States UC Davis Eye-Pod Small Animal Ocular Imaging Laboratory Department of Cell Biology and Human Anatomy University of California Davis 4320 Tupper Hall DavisCA95616 United States

ISBN: (纸本)9781510669079

While the a-wave of mouse electroretinogram (ERG) occurs within 50 milliseconds after exposure to light, the optoretinogram (ORG) slower than a 20Hz sampling rate could face limitations in observing immediate morphological changes from the retina. In this study, we develop a compact custom-built mouse ORG system based on spectral domain optical coherence tomography (SD-OCT) for 100Hz~1KHz B-scan rates comprised of 100 kHz A-scans. All the optics of the developed ORG system are designed on a 24 x 24 inches optical breadboard to move easily as well as to combine with the ERG system in a dark room. Without using a fundus camera, the OCT system provides en-face images from high-pass filtering and square of the OCT spectral signal for mouse retinal positioning in-vivo before acquiring ORG data. The 490nm LED for light stimulus is generated to make uniform illumination at the mouse retina using the Maxwellian view method. The common path of the OCT scanning light and the visible LED is built with achromatic doublet lens combinations based on optical simulation with Opticstudio® . The developed compact ORG system can not only observe light-evoked responses with 1~10 milliseconds but also be used for the studies of correlations between ORG and ERG in the mouse retina. © 2024 SPIE.

关键词： Optical tomography

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iFBI: Lightweight Breed and Individual Recognition for Cats and Dogs

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhang, Yuanze Zhang, Yimeng Li, Kexu Luo, Jinpeng Liu, Gang Pan, Rong Xidian University Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province School of Computer Science and Technology Xi’an 710126 China

As the pet industry develops, fine-grained breed recognition and individual recognition have emerged as essential components in biometric measurement systems for intelligent pet monitoring, aiming to identify the specific breed of a pet in an image and to recognize the same individual across multiple images. These capabilities lay the foundation for downstream tasks such as posture estimation and emotion analysis, supporting a wide range of real-world applications. Despite the substantial advancements achieved in existing research, two critical issues remain to be solved: the diversity of object poses affects representation in complex scenarios, and the conflict between model complexity and performance hinders application in resource-constrained conditions. To address the above issues, we propose an integrated Face-and-Body Information for Lightweight Breed and Individual Recognition (iFBI) scheme that integrates multiple pose information by a lightweight model. Specifically, a Face Alignment (FA) module and a Body Posture-Guided (BPG) module are proposed to separate face and body information from the input images, fully capturing the posture details while suppressing background areas. To further maximize the discrimination between individual samples, we design a Multi-level Representation Recognition (MRR) module that dynamically integrates complementary semantic features of face and body, consequently generating more discriminative features. In addition, a Dynamic Convolutional Model Compression (DCMC) method is implemented with an improved dual-branch backbone that significantly reduces computational costs while enhancing model performance. Extensive experiments on two self-built datasets, Pet with Fine-grained Breed Dataset (Pet-FB) and Pet with Diverse Posture Dataset (Pet-DP), and four public datasets indicate that iFBI yields superior performance in both fine-grained breed recognition and individual recognition tasks. The source code and self-built datasets—P

关键词： Face alignment Fine-grained breed recognition Individual recognition Lightweight

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