Smart Health最新文献_第2页

Dynamic fog computing for enhanced LLM execution in medical applications 在医疗应用中增强LLM执行的动态雾计算

Smart Health Pub Date : 2025-04-02 DOI: 10.1016/j.smhl.2025.100577

Philipp Zagar , Vishnu Ravi , Lauren Aalami , Stephan Krusche , Oliver Aalami , Paul Schmiedmayer

{"title":"Dynamic fog computing for enhanced LLM execution in medical applications","authors":"Philipp Zagar , Vishnu Ravi , Lauren Aalami , Stephan Krusche , Oliver Aalami , Paul Schmiedmayer","doi":"10.1016/j.smhl.2025.100577","DOIUrl":"10.1016/j.smhl.2025.100577","url":null,"abstract":"<div><div>The ability of large language models (LLMs) to process, interpret, and comprehend vast amounts of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected health information (PHI) raises concerns about data privacy and trust in remote LLM platforms. Additionally, the cost of cloud-based artificial intelligence (AI) services remains a barrier to widespread adoption. To address these challenges, we propose shifting the LLM execution environment from centralized, opaque cloud providers to a decentralized and dynamic fog computing architecture. By running open-weight LLMs in more trusted environments, such as a user’s edge device or a fog layer within a local network, we aim to mitigate the privacy, trust, and financial concerns associated with cloud-based LLMs. We introduce <em>SpeziLLM</em>, an open-source framework designed to streamline LLM execution across multiple layers, facilitating seamless integration into digital health applications. To demonstrate its versatility, we showcase <em>SpeziLLM</em> across six digital health applications, highlighting its broad applicability in various healthcare settings.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation 基于贝叶斯先验估计的异质感知室内定位

Smart Health Pub Date : 2025-03-28 DOI: 10.1016/j.smhl.2025.100559

Changming Li , Cong Shi , Yingying Chen , David Maluf , Jerome Henry

{"title":"Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation","authors":"Changming Li , Cong Shi , Yingying Chen , David Maluf , Jerome Henry","doi":"10.1016/j.smhl.2025.100559","DOIUrl":"10.1016/j.smhl.2025.100559","url":null,"abstract":"<div><div>Indoor wireless localization is critical for enabling a wide range of mobile and IoT applications, such as elder monitoring, robot navigation, and augmented reality/virtual reality. Current wireless localization techniques rely on homogeneous sensing, utilizing single-modality signals like Bluetooth, WiFi, or mmWave, which are susceptible to in-channel interference, multipath distortions, and environmental variability (e.g., device position and furniture placement changes). In this paper, we design a heterogeneous sensing system that combines wireless signals of multiple modalities to enhance indoor localization accuracy. Through building a Bayesian-based framework, we statistically integrate location fingerprints from various sensing modalities to address the nonlinearities introduced by spatial and temporal fluctuations. Our approach is generalizable and can be applied to existing fingerprinting localization methods based on machine learning algorithms, such as K-nearest neighbors (KNN), support vector machines (SVM), and deep learning models, significantly enhancing the localization performance and robustness. Extensive real-world experiments demonstrate that our system reduces the average localization errors from 2.1 m to 1.23 m, even in the presence of complex environmental dynamics.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100559"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Objective anxiety level classification using unsupervised learning and multimodal physiological signals 目的利用无监督学习和多模态生理信号对焦虑水平进行分类

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100572

Maxine He , Jonathan Cerna , Roshni Mathew , Jiaqi Zhao , Jennifer Zhao , Ethan Espina , Jean L. Clore , Richard B. Sowers , Elizabeth T. Hsiao-Wecksler , Manuel E. Hernandez

{"title":"Objective anxiety level classification using unsupervised learning and multimodal physiological signals","authors":"Maxine He , Jonathan Cerna , Roshni Mathew , Jiaqi Zhao , Jennifer Zhao , Ethan Espina , Jean L. Clore , Richard B. Sowers , Elizabeth T. Hsiao-Wecksler , Manuel E. Hernandez","doi":"10.1016/j.smhl.2025.100572","DOIUrl":"10.1016/j.smhl.2025.100572","url":null,"abstract":"<div><div>Anxiety disorders are prevalent worldwide and can negatively impact physical and mental health. Thus, the timely detection of changes in anxiety levels is crucial for mental health management. This study used multimodal physiological features from wearable devices to classify anxiety levels across various conditions, normalized by individual baseline responses for personalized analysis. Gaussian Mixture Models clustered data into binary or ternary anxiety levels, interpreted by statistics of self-reported scores and physiological features. Clus ters showed modest alignment with State and Trait Inventory scores and physiological markers and demonstrated task-specific variability. Silhouette scores indicated moderate separation (0.40 for two clusters, 0.14 for three clusters). Binary and three-class classifications using unsupervised learning and leave-one-participant-out validation demonstrated effectiveness, with Support Vector Machine achieving highest accuracies (90.9% and 73.3%). This approach enables objective, personalized anxiety monitoring without relying on subjective labeling.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100572"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intoxication detection from speech using representations learned from self-supervised pre-training 使用自监督预训练学习表征的语音中毒检测

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100562

Abigail Albuquerque, Samuel Chibuoyim Uche, Emmanuel Agu

{"title":"Intoxication detection from speech using representations learned from self-supervised pre-training","authors":"Abigail Albuquerque, Samuel Chibuoyim Uche, Emmanuel Agu","doi":"10.1016/j.smhl.2025.100562","DOIUrl":"10.1016/j.smhl.2025.100562","url":null,"abstract":"<div><div>Alcohol intoxication is one of the leading causes of death around the globe. Existing approaches to prevent Driving Under the Influence (DUI) are expensive, intrusive, or require external apparatus such as breathalyzers, which the drinker may not possess. Speech is a viable modality for detecting intoxication from changes in vocal patterns. Intoxicated speech is slower, has lower amplitude, and is more prone to errors at the sentence, word, and phonological levels than sober speech. However, intoxication detection from speech is challenging due to high inter- and intra-user variability and the confounding effects of other factors such as fatigue, which may also impair speech. This paper investigates Wav2Vec 2.0, a self-supervised neural network architecture, for intoxication classification from audio. Wav2Vec 2.0 is a Transformer-based model that has demonstrated remarkable performance in various speech-related tasks. It analyzes raw audio directly by applying a multi-head attention mechanism to latent audio representations and was pre-trained on the Librispeech, Libri-Light and EmoDB datasets. The proposed model achieved an unweighted average recall of 73.3%, outperforming state-of-the-art models, highlighting its potential for accurate DUI detection to prevent alcohol-related incidents.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100562"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADHDSymTracker: Predicting ADHD Symptoms using Apple HealthKit Data ADHDSymTracker：使用Apple HealthKit数据预测ADHD症状

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100563

Shweta Ware , Allison Baun , Peiyi Wang , Caleb Kwakye , Sofia Dimotsi , Ethan Swift , Nikoloz Gvelesiani , Laura E. Knouse

{"title":"ADHDSymTracker: Predicting ADHD Symptoms using Apple HealthKit Data","authors":"Shweta Ware , Allison Baun , Peiyi Wang , Caleb Kwakye , Sofia Dimotsi , Ethan Swift , Nikoloz Gvelesiani , Laura E. Knouse","doi":"10.1016/j.smhl.2025.100563","DOIUrl":"10.1016/j.smhl.2025.100563","url":null,"abstract":"<div><div>Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent condition that impacts cognitive and behavioral functioning, posing significant challenges for individuals’ academic and daily lives, particularly among college students. The core symptoms are inattention, hyperactivity and impulsivity. Current diagnostic and symptom tracking methods, whether clinician-administered or self-reported, have several limitations, such as recall bias, high costs, and the necessity for manual intervention. This underscores the necessity for an objective, accurate, and cost-effective tool for ADHD diagnosis that requires minimal manual intervention. To address this issue, we propose a novel approach, ADHDSymTracker, which uses Apple HealthKit data to predict ADHD symptoms. We calculated behavioral features using data collected from 38 college-age students including some with ADHD and developed a suite of machine learning models for ADHD symptom prediction. Our results from ADHDSymTracker indicate that most symptoms can be predicted with reasonable accuracy, achieving an <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score as high as 0.72, rendering it a promising solution for automatic and continuous ADHD monitoring.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100563"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A low-channel EEG-to-speech conversion approach for assisting people with communication disorders 一种帮助有沟通障碍人士的低通道脑电图-语言转换方法

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100568

Kunning Shen , Huining Li

引用次数: 0

Background parenchymal uptake classification using deep transfer learning on digital mammograms 背景：基于深度迁移学习的数字化乳房x光片实质摄取分类

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100573

Xudong Liu , Christopher Scott , Imon Banerjee , Celine Vachon , Carrie Hruska

{"title":"Background parenchymal uptake classification using deep transfer learning on digital mammograms","authors":"Xudong Liu , Christopher Scott , Imon Banerjee , Celine Vachon , Carrie Hruska","doi":"10.1016/j.smhl.2025.100573","DOIUrl":"10.1016/j.smhl.2025.100573","url":null,"abstract":"<div><div>Background parenchymal uptake (BPU) in fibroglandular tissue on a molecular breast image (MBI) has been shown to be a strong risk factor for breast cancer and complementary to mammographic density. However, MBI is generally performed on women with dense breasts and only available at institutions with nuclear medicine capabilities, limiting the utility of this measure in routine breast screening and risk assessment. Digital mammography is used for routine breast screening. Our goal was to evaluate whether BPU features could be identified from digital mammograms (DMs) using deep transfer learning. Specifically, we identified a cohort of about 2000 women from a breast screening center who had DM and MBI performed at the same time period and trained models on DMs to classify BPU categories. We consider two types of classification problems in this work: a five-category classification of BPU and two combined classes. We designed and implemented machine learning algorithms leveraging state-of-the-art pre-trained deep neural networks, evaluated these algorithms on the collected data based using metrics such as accuracy, F1-score, and AUROC, and provided visual explanations using saliency mapping and gradient-weighted class activation mapping (GradCAM). Our results show that, among the experimented models, WideResNet-50 demonstrates the best performance on a hold-out test set with 58% accuracy, 0.82 micro-average AUROC and 0.72 macro-average AUROC on the five-category classification, while ResNet-18 comes out on top with 77% accuracy, 0.86 AUROC and 0.77 F1-score on the binary categorization. We also found that incorporating age, body mass index (BMI) and menopausal status improved classification of BPU compared to DM alone.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100573"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SNOMED CT ontology multi-relation classification by using knowledge embedding in neural network 基于神经网络知识嵌入的SNOMED CT本体多关系分类

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100560

Bofan He, Jerry Q. Cheng, Huanying Gu

{"title":"SNOMED CT ontology multi-relation classification by using knowledge embedding in neural network","authors":"Bofan He, Jerry Q. Cheng, Huanying Gu","doi":"10.1016/j.smhl.2025.100560","DOIUrl":"10.1016/j.smhl.2025.100560","url":null,"abstract":"<div><div>SNOMED CT is a widely recognized healthcare terminology designed to comprehensively represent clinical knowledge. Identifying missing or incorrect relationships between medical concepts is crucial for enhancing the scope and quality of this ontology, thereby improving healthcare analytics and decision support. In this study, we propose a novel multi-link prediction approach that utilizes knowledge graph embeddings and neural networks to infer missing relationships within the SNOMED CT knowledge graph. By utilizing TransE, we train embeddings for triples (concept, relation, concept) and develop a multi-head classifier to predict relationship types based solely on concept pairs. With an embedding dimension of 200, a batch size of 128, and 10 epochs, we achieved the highest test accuracy of 91.96% in relationships prediction tasks. This study demonstrates an optimal balance between efficiency, generalization, and representational capacity. By expanding on existing methodologies, this work offers insights into practical applications for ontology enrichment and contributes to the ongoing advancement of predictive models in healthcare informatics. Furthermore, it highlights the potential scalability of the approach, providing a framework that can be extended to other knowledge graphs and domains.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mobile app-based study of driving behaviors under the influence of cannabis 基于手机app的大麻影响下驾驶行为研究

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100558

Honglu Li , Bin Han , Cong Shi , Yan Wang , Tammy Chung , Yingying Chen

引用次数: 0

Exploring finetuned audio-LLM on heart murmur features 探索微调音频- llm心脏杂音的特点

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100557

Adrian Florea, Xilin Jiang, Nima Mesgarani, Xiaofan Jiang

{"title":"Exploring finetuned audio-LLM on heart murmur features","authors":"Adrian Florea, Xilin Jiang, Nima Mesgarani, Xiaofan Jiang","doi":"10.1016/j.smhl.2025.100557","DOIUrl":"10.1016/j.smhl.2025.100557","url":null,"abstract":"<div><div>Large language models (LLMs) for audio have excelled in recognizing and analyzing human speech, music, and environmental sounds. However, their potential for understanding other types of sounds, particularly biomedical sounds, remains largely underexplored despite significant scientific interest. In this study, we focus on diagnosing cardiovascular diseases using phonocardiograms, i.e., heart sounds. Most existing deep neural network (DNN) paradigms are restricted to heart murmur classification (healthy vs unhealthy) and do not predict other acoustic features of the murmur such as grading, harshness, pitch, and quality, which are important in helping physicians diagnose the underlying heart conditions. We propose to finetune an audio LLM, Qwen2-Audio, on the PhysioNet CirCor DigiScope phonocardiogram (PCG) dataset and evaluate its performance in classifying 11 expert-labeled features. Additionally, we aim to achieve more noise-robust and generalizable system by exploring a preprocessing segmentation algorithm using an audio representation model, SSAMBA. Our results indicate that the LLM-based model outperforms state-of-the-art methods in 10 of the 11 tasks. Moreover, the LLM successfully classifies long-tail features with limited training data, a task that all previous methods have failed to classify. These findings underscore the potential of audio LLMs as assistants to human cardiologists in enhancing heart disease diagnosis.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0