Smart Health最新文献

{"title":"HyperGAN: A novel approach for the biomedical cancer text documents classification using Deep Hypergraph Attention Network","authors":"S. Pradeepa ,&nbsp;K. Ramya ,&nbsp;S. Vimal ,&nbsp;P. Subbulakshmi ,&nbsp;Gaurav Dhiman ,&nbsp;Tarik A. Rashid ,&nbsp;Aso M. Aladdin","doi":"10.1016/j.smhl.2025.100598","DOIUrl":"10.1016/j.smhl.2025.100598","url":null,"abstract":"<div><div>The exponential growth in biomedical research has induced a substantial volume of scientific literature, posing a formidable challenge in extracting meaningful insights. The challenge is particularly profound in the cancer research domain, where the continuous influx of research necessitates efficient classification techniques to analyze relevant findings. To address this issue, this study introduces a novel approach for the systematic classification of cancer-related research publications indexed in PubMed. A Hypergraph Attention Neural Network is proposed to automate the classification ofBiomedical Cancer Text Documents focusing on the identification of cancer categories. This model enhances the classification of three distinct categories of cancer documents, namely lung, thyroid, and colon, obtained from a Kaggle dataset consisting of 7570 cancer research publications sourced from PubMed. The study employs the Latent Dirichlet Allocation (LDA) technique for keyword extraction, while the node-level and edge-level attention mechanisms within the hypergraph structure optimize classification performance. The proposed classification model achieves an exceptional accuracy of 99.7 %, surpassing other cutting-edge text classifiers. The findings underscore the potential of exploring text-mining techniques in cancer-focused literature, paving the way for advanced research and knowledge discovery.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100598"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780921","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}

{"title":"ML-predicted surgical site infections: An epidemiological study utilizing machine learning on routinely collected healthcare data to predict infection risk","authors":"Davide Golinelli ,&nbsp;Simona Rosa ,&nbsp;Paola Rucci ,&nbsp;Francesco Sanmarchi ,&nbsp;Dario Tedesco ,&nbsp;Carlo Biagetti ,&nbsp;Alessio Gili ,&nbsp;Andrea Bucci ,&nbsp;Luca Romeo ,&nbsp;Roberto Grilli","doi":"10.1016/j.smhl.2025.100596","DOIUrl":"10.1016/j.smhl.2025.100596","url":null,"abstract":"<div><h3>Background</h3><div>Surgical site infections (SSIs) are a major public health issue, causing increased morbidity, longer hospital stays, and higher healthcare costs. Despite progress in infection control, predicting and preventing SSIs remain crucial for improving patient outcomes. This study examines the use of machine learning (ML) on routinely collected healthcare data (RCD) to predict SSIs in orthopaedic surgery, aiming to improve risk stratification and guide interventions.</div></div><div><h3>Objectives</h3><div>To develop, test, and validate an ML predictive model using RCD to assess SSI risk in orthopaedic surgery patients.</div></div><div><h3>Methods</h3><div>A retrospective study was carried out using RCD from a 1.2 million population in an Italian Local Health Authority, covering surgeries from 2017 to 2021. The population included patients undergoing hip or knee arthroplasty and open reduction of fractures. Several ML algorithms, including eXtreme Gradient Boosting (XGBoost), were used for model development. The models’ performance was assessed by recall, accuracy, and area under the receiver operating characteristic curve (AUC). A feature importance analysis identified key SSI risk predictors.</div></div><div><h3>Results</h3><div>The XGBoost model demonstrated superior performance, with a recall exceeding 70% and an AUC&gt;0.70, overcoming other methods. Significant predictors included the ASA classification, opioid use, priority class of the surgery operation, and length of hospital stay.</div></div><div><h3>Conclusions</h3><div>ML models, particularly XGBoost, effectively predicted SSI risk in orthopaedic patients, offering a new approach to infection control and prevention. Incorporating ML and RCD highlights the potential for scalable, data-driven personalized medicine interventions. Future research will focus on model validation and integration of these tools into healthcare systems for enhanced patient management.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100596"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518492","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}

{"title":"Technoethics and recommendations for technological interventions to reduce loneliness in older adults","authors":"Emma Cho,&nbsp;Connie M. Ulrich","doi":"10.1016/j.smhl.2025.100583","DOIUrl":"10.1016/j.smhl.2025.100583","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100583"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116780","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}

{"title":"Human knowledge-based artificial intelligence methods for skin cancer management: Accuracy and interpretability study","authors":"Eman Rezk ,&nbsp;Mohamed Eltorki ,&nbsp;Wael El-Dakhakhni","doi":"10.1016/j.smhl.2025.100540","DOIUrl":"10.1016/j.smhl.2025.100540","url":null,"abstract":"<div><div>Skin cancer management, including monitoring and excision, involves sophisticated decisions reliant on several interdependent factors. This complexity leads to a scarcity of data useful for skin cancer management. Deep learning achieved massive success in computer vision due to its ability to extract representative features from images. However, deep learning methods require large amounts of data to develop accurate models, whereas machine learning methods perform well with small datasets. In this work, we aim to compare the accuracy and interpretability of skin cancer management prediction 1) using deep learning and machine learning methods and 2) utilizing various inputs including clinical images, dermoscopic images, and lesion clinical tabular features created by experts to represent lesion characteristics. We implemented two approaches, a deep learning pipeline for feature extraction and classification trained on different input modalities including images and lesion clinical features. The second approach uses lesion clinical features to train machine learning classifiers. The results show that the machine learning approach trained on clinical features achieves higher accuracy (0.80) and higher area under the curve (0.92) compared to the deep learning pipeline trained on skin images and lesion clinical features which achieves an accuracy of 0.66 and area under the curve of 0.74. Additionally, the machine learning approach provides more informative and understandable interpretations of the results. This work emphasizes the significance of utilizing human knowledge in developing precise and transparent predictive models. In addition, our findings highlight the potential of machine learning methods in predicting lesion management in situation where the data size is insufficient to leverage deep learning capabilities.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100540"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100542","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}

{"title":"PulseSight : A novel method for contactless oxygen saturation (SpO2) monitoring using smartphone cameras, remote photoplethysmography and machine learning","authors":"Kazi Zawad Arefin ,&nbsp;Kazi Shafiul Alam ,&nbsp;Sayed Mashroor Mamun ,&nbsp;Nafi Us Sabbir Sabith ,&nbsp;Masud Rabbani ,&nbsp;Parama Sridevi ,&nbsp;Sheikh Iqbal Ahamed","doi":"10.1016/j.smhl.2025.100542","DOIUrl":"10.1016/j.smhl.2025.100542","url":null,"abstract":"<div><div>Monitoring oxygen saturation (SpO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) level is crucial for evaluating the current cardiac and respiratory condition of a person, particularly in medical settings. Conventional pulse oximetry, while efficient, has drawbacks such as the requirement for physical touch and vulnerability to certain environmental influences. In this paper, we propose an innovative approach for estimating SpO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> levels utilizing smartphone cameras and video-based photoplethysmography (PPG) without physical touch. Our framework consists of an Android mobile application that records 20-second face videos, which a cloud-based backend server then analyzes. The server utilizes deep learning-based facial recognition and signal processing techniques to extract remote photoplethysmography (rPPG) signals from specific facial regions and predict oxygen saturation (SpO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) levels using a Support Vector Regression (SVR) Machine learning model. Signal noise and motion artifacts are mitigated by extracting relevant characteristics from the rPPG. The system was validated by experimental studies, which contained 40 sets of videos collected from 10 participants. The study was conducted under different illumination conditions, which showed low RMSE score (1.45 ±0.1) and MAE score (0.92 ±0.01). Also, our system shows high usability, as indicated by the System Usability Scale (SUS) score of 80.5. The results demonstrate that our method offers a dependable and contactless substitute for continuous SpO2 monitoring, with potential uses in telemedicine and remote patient monitoring.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100542"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395171","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}

{"title":"Intuitive axial augmentation using polar-sine-based piecewise distortion for medical slice-wise segmentation","authors":"Yiqin Zhang ,&nbsp;Qingkui Chen ,&nbsp;Chen Huang ,&nbsp;Zhengjie Zhang ,&nbsp;Meiling Chen ,&nbsp;Zhibing Fu","doi":"10.1016/j.smhl.2025.100556","DOIUrl":"10.1016/j.smhl.2025.100556","url":null,"abstract":"<div><div>Most data-driven models for medical image analysis rely on universal augmentations to improve accuracy. Experimental evidence has confirmed their effectiveness, but the unclear mechanism underlying them poses a barrier to the widespread acceptance and trust in such methods within the medical community. We revisit and acknowledge the unique characteristics of medical images apart from traditional digital images, and consequently, proposed a medical-specific augmentation algorithm that is more elastic and aligns well with radiology scan procedure. The method performs piecewise affine with sinusoidal distorted ray according to radius on polar coordinates, thus simulating uncertain postures of human lying flat on the scanning table. Our method could generate human visceral distribution without affecting the fundamental relative position on axial plane. Two non-adaptive algorithms, namely Meta-based Scan Table Removal and Similarity-Guided Parameter Search, are introduced to bolster robustness of our augmentation method. In contrast to other methodologies, our method is highlighted for its intuitive design and ease of understanding for medical professionals, thereby enhancing its applicability in clinical scenarios. Experiments show our method improves accuracy with two modality across multiple famous segmentation frameworks without requiring more data samples. Our preview code is available in: <span><span>https://github.com/MGAMZ/PSBPD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100556"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696894","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}

{"title":"Clinically relevant predictive modeling for personalized ACL reconstruction classification","authors":"Xishi Zhu ,&nbsp;Ryan Henry ,&nbsp;Emily Jackson ,&nbsp;Joe M. Hart ,&nbsp;Jiaqi Gong","doi":"10.1016/j.smhl.2025.100575","DOIUrl":"10.1016/j.smhl.2025.100575","url":null,"abstract":"<div><div>Anterior Cruciate Ligament (ACL) reconstruction outcomes and return-to-sport readiness vary significantly among patients, yet current classification methods often lack interpretability and personalization. We propose an explainable predictive model for ACL reconstruction classification through multi-modal analysis of gait dynamics and patient characteristics. Using inertial measurement unit (IMU) sensors on participants’ wrists, ankles, and sacrum, we collected gait data during walking and jogging tasks, alongside patient-specific survey information. For gait dynamics, we employed Phase Slope Index to quantify inter-sensor relationships and trained classifiers for different ACL reconstruction outcomes(left vs right injury, healthy vs injured), achieving high classification performance (96.37% accuracy). Model explanations using heatmaps and permutation importance revealed that paired body movements are crucial in classification, with more distinct patterns in jogging than walking. For patient characteristics, t-SNE visualization demonstrated that model confidence correlated strongly with recovery duration. While longer recovery typically leads to more normal gait patterns, our approach provides a quantitative method to visualize this process transparently. This explainable, personalized approach can improve rehabilitation strategies and inform more accurate return-to-sport decisions in sports medicine.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100575"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895599","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}

{"title":"Editorial – Elsevier smart health special issue: Advancing ICT for health, accessibility, and wellbeing","authors":"Achilleas Achilleos ,&nbsp;Edwige Pissaloux ,&nbsp;George A. Papadopoulos ,&nbsp;Ramiro Velazquez","doi":"10.1016/j.smhl.2025.100580","DOIUrl":"10.1016/j.smhl.2025.100580","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100580"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947103","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}

{"title":"A low-channel EEG-to-speech conversion approach for assisting people with communication disorders","authors":"Kunning Shen ,&nbsp;Huining Li","doi":"10.1016/j.smhl.2025.100568","DOIUrl":"10.1016/j.smhl.2025.100568","url":null,"abstract":"<div><div>Brain–Computer Interface (BCI) technology has emerged as a promising solution for individuals with communication disorders. However, current electroencephalography (EEG) to speech systems typically require high-channel EEG equipment (64＋ channels), limiting their accessibility in resource-constrained environments. This paper implements a novel low-channel EEG-to-speech framework that effectively operates with only 6 EEG channels. By leveraging a generator-discriminator architecture for speech reconstruction, our system achieves a Character Error Rate (CER) of 64.24%, outperforming baseline systems that utilize 64 channels (68.26% CER). We further integrate Undercomplete Independent Component Analysis (UICA) for channel reduction, maintaining comparable accuracy (64.99% CER) while reducing computational complexity from 6 channels to 4 channels. This breakthrough demonstrates the feasibility of efficient speech reconstruction from minimal EEG inputs, potentially enabling more widespread deployment of BCI technology in resource-limited healthcare settings.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100568"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725640","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}

{"title":"Transforming stroop task cognitive assessments with multimodal inverse reinforcement learning","authors":"Ali Abbasi ,&nbsp;Jiaqi Gong ,&nbsp;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":"2025-06-01","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}