Smart Health最新文献_第2页

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

An adaptive multimodal fusion framework for smartphone-based medication adherence monitoring of Parkinson’s disease 基于智能手机的帕金森病药物依从性监测的自适应多模式融合框架

Smart Health Pub Date : 2025-03-25 DOI: 10.1016/j.smhl.2025.100561

Chongxin Zhong , Jinyuan Jia , Huining Li

{"title":"An adaptive multimodal fusion framework for smartphone-based medication adherence monitoring of Parkinson’s disease","authors":"Chongxin Zhong , Jinyuan Jia , Huining Li","doi":"10.1016/j.smhl.2025.100561","DOIUrl":"10.1016/j.smhl.2025.100561","url":null,"abstract":"<div><div>Ensuring medication adherence for Parkinson’s disease (PD) patients is crucial to relieve patients’ symptoms and better customizing regimens according to patient’s clinical responses. However, traditional self-management approaches are often error-prone and have limited effectiveness in improving adherence. While smartphone-based solutions have been introduced to monitor various PD metrics, including medication adherence, these methods often rely on single-modality data or fail to fully leverage the advantages of multimodal integration. To address the issues, we present an adaptive multimodal fusion framework for monitoring medication adherence of PD based on a smartphone. Specifically, we segment and transform raw data from sensors to spectrograms. Then, we integrate multimodal data with quantification of their qualities and perform gradient modulation based on the contribution of each modality. Afterward, we monitor medication adherence in PD patients by detecting their medicine intake status. We evaluate the performance with the dataset from daily-life scenarios involving 455 patients. The results show that our work can achieve around 94% accuracy in medication adherence monitoring, indicating that our proposed framework is a promising tool to facilitate medication adherence monitoring in PD patients’ daily lives.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100561"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704184","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

Transforming stroop task cognitive assessments with multimodal inverse reinforcement learning 用多模态逆强化学习转化部队任务认知评估

Smart Health Pub Date : 2025-03-25 DOI: 10.1016/j.smhl.2025.100567

Ali Abbasi , Jiaqi Gong , Soroush Korivand

{"title":"Transforming stroop task cognitive assessments with multimodal inverse reinforcement learning","authors":"Ali Abbasi , Jiaqi Gong , Soroush Korivand","doi":"10.1016/j.smhl.2025.100567","DOIUrl":"10.1016/j.smhl.2025.100567","url":null,"abstract":"<div><div>Stroop tasks, recognized for their cognitively demanding nature, hold promise for diagnosing and monitoring neurodegenerative diseases. Understanding how humans allocate attention and resolve interference in the Stroop test remains a challenge; yet addressing this gap could reveal key opportunities for early-stage detection. Traditional approaches overlook the interplay between overt behavior and underlying neural processes, limiting insights into the complex color-word associations at play. To tackle this, we propose a framework that applies Inverse Reinforcement Learning (IRL) to fuse electroencephalography (EEG) signals with eye-tracking data, bridging the gap between neural and behavioral markers of cognition. We designed a Stroop experiment featuring congruent and incongruent conditions to evaluate attention allocation under varying levels of interference. By framing gaze as actions guided by an internally derived reward, IRL uncovers hidden motivations behind scanning patterns, while EEG data — processed with advanced feature extraction — reveals task-specific neural dynamics under high conflict. We validate our approach by measuring Probability Mismatch, Target Fixation Probability-Area Under the Curve, Sequence Score, and MultiMatch metrics. Results show that the IRL-EEG model outperforms an IRL-Image baseline, demonstrating improved alignment with human scanpaths and heightened sensitivity to attentional shifts in incongruent trials. These findings highlight the value of integrating neural data into computational models of cognition and illuminate possibilities for early detection of neurodegenerative disorders, where subclinical deficits may first emerge. Our IRL-based integration of EEG and eye-tracking further supports personalized cognitive assessments and adaptive user interfaces.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100567"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725638","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

Improving gastric lesion detection with synthetic images from diffusion models 利用扩散模型合成图像改进胃病变检测

Smart Health Pub Date : 2025-03-25 DOI: 10.1016/j.smhl.2025.100569

Yanhua Si , Yingyun Yang , Qilei Chen , Zinan Xiong , Yu Cao , Xinwen Fu , Benyuan Liu , Aiming Yang

{"title":"Improving gastric lesion detection with synthetic images from diffusion models","authors":"Yanhua Si , Yingyun Yang , Qilei Chen , Zinan Xiong , Yu Cao , Xinwen Fu , Benyuan Liu , Aiming Yang","doi":"10.1016/j.smhl.2025.100569","DOIUrl":"10.1016/j.smhl.2025.100569","url":null,"abstract":"<div><div>In the application of deep learning for gastric cancer detection, the quality of the data set is as important as, if not more, the design of the network architecture. However, obtaining labeled data, especially in fields such as medical imaging to detect gastric cancer, can be expensive and challenging. This scarcity is exacerbated by stringent privacy regulations and the need for annotations by specialists. Conventional methods of data augmentation fall short due to the complexities of medical imagery. In this paper, we explore the use of diffusion models to generate synthetic medical images for the detection of gastric cancer. We evaluate their capability to produce realistic images that can augment small datasets, potentially enhancing the accuracy and robustness of detection algorithms. By training diffusion models on existing gastric cancer data and producing new images, our aim is to expand these datasets, thereby enhancing the efficiency of deep learning model training to achieve better precision and generalization in lesion detection. Our findings indicate that images generated by diffusion models significantly mitigate the issue of data scarcity, advancing the field of deep learning in medical imaging.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100569"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738102","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

Continuous prediction of user dropout in a mobile mental health intervention program: An exploratory machine learning approach 移动心理健康干预项目中用户退出的连续预测：一种探索性机器学习方法

Smart Health Pub Date : 2025-03-25 DOI: 10.1016/j.smhl.2025.100565

Pinxiang Wang , Hanqi Chen , Zhouyu Li , Wenyao Xu , Yu-Ping Chang , Huining Li

引用次数: 0