Journal of healthcare informatics research最新文献

{"title":"Recognition of Daily Activities Through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living.","authors":"Kooshan Hashemifard, Pau Climent-Pérez, Francisco Florez-Revuelta","doi":"10.1007/s41666-026-00244-w","DOIUrl":"10.1007/s41666-026-00244-w","url":null,"abstract":"<p><p>Recognition of daily activities is a critical element for effective Ambient Assisted Living (AAL) systems, particularly to monitor the well-being and support the independence of older adults in indoor environments. However, developing robust activity recognition systems faces significant challenges, including intra-class variability, inter-class similarity, environmental variability, camera perspectives, and scene complexity. This paper presents a multi-modal approach for the recognition of activities of daily living tailored for older adults within AAL settings. The proposed system integrates visual information processed by a 3D Convolutional Neural Network (CNN) with 3D human pose data analyzed by a Graph Convolutional Network. Contextual information, derived from an object detection module, is fused with the 3D CNN features using a cross-attention mechanism to enhance recognition accuracy. This method is evaluated using the Toyota SmartHome dataset, which consists of real-world indoor activities. The results indicate that the proposed system achieves competitive classification accuracy for a range of daily activities, highlighting its potential as an essential component for advanced AAL monitoring solutions. This advancement supports the broader goal of developing intelligent systems that promote safety and autonomy among older adults.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"565-595"},"PeriodicalIF":4.6,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852169","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}

{"title":"A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.","authors":"Jinqian Pan, Tienyu Chang, Mengxian Lyu, Weimin Meng, Qingyu Wang, Yiling Ma, Ziyi Chen, Xiaohan Li, Chengkun Sun, Renjie Liang, Jennifer Fishe, Jie Xu","doi":"10.1007/s41666-026-00248-6","DOIUrl":"10.1007/s41666-026-00248-6","url":null,"abstract":"<p><p>Artificial intelligence (AI) is increasingly applied in healthcare to support diagnosis and treatment by leveraging longitudinal multimodal data, despite persistent challenges such as missing data and heterogeneity. This systematic review, conducted in accordance with PRISMA guidelines, analyzed deep learning (DL) and machine learning (ML) applications to longitudinal multimodal clinical data published between 2020 and 2024. An initial search identified 1,124 records; a total of 54 items were included (53 studies meeting inclusion criteria and 1 dataset descriptor). Relevant research studies increased substantially over the review period, from 3 studies in 2020 to 21 in 2024. Prediction (n = 23) and classification (n = 19) were the most commonly addressed tasks. Structured electronic health records (EHRs) and medical imaging were the most frequently used data sources, reported in 48 and 33 studies, respectively, and were often used in combination. Regarding missing data, 34 studies reported general handling strategies, and 10 explicitly addressed missing modalities. While methodological variability and performance limitations remain, these findings highlight the growing use of DL and ML to analyze multimodal data, which may inform clinical decision support.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00248-6.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"627-653"},"PeriodicalIF":4.6,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852088","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}

{"title":"Detection and Early Detection of Pathological Motor Evoked Potentials: A Novel Approach for Intraoperative Neuromonitoring Assistance.","authors":"Alberto Azzari, Alessandro Boaro, Federica Basaldella, Sonia Nunes, Francesco Sala, Carlo Combi, Pietro Sala, Manuele Bicego","doi":"10.1007/s41666-026-00245-9","DOIUrl":"10.1007/s41666-026-00245-9","url":null,"abstract":"<p><p>Intraoperative monitoring of motor evoked potentials (MEPs) plays a crucial role in detecting potential neurological complications during surgical procedures. Traditional methods rely on real-time human interpretation, which can introduce delays or, worse, errors in response. This study proposes an early detection framework for pathological MEPs, leveraging an anomaly detection approach based on an extension of isolation forests-one of the most effective anomaly detection models. By modeling the temporal evolution of MEPs, our approach not only identifies pathological deviations but also anticipates their occurrence, enabling proactive intervention. We validated our methodology on a dataset of intraoperative MEP recordings, demonstrating improved detection performance and enhanced early warning capabilities compared to conventional approaches. The findings suggest that this framework may support real-time intraoperative monitoring and serve as a foundation for future decision-support tools, although prospective clinical validation is still required before any deployment can be considered.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"596-626"},"PeriodicalIF":4.6,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852070","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}

{"title":"Leveraging LLMs for Title and Abstract Screening for Systematic Review: A Cost-effective Dynamic Few-shot Learning Approach.","authors":"Yun-Chung Liu, Rui Yang, Jonathan Chong Kai Liew, Ziran Yin, Henry P Foote, Christopher J Lindsell, Chuan Hong","doi":"10.1007/s41666-026-00246-8","DOIUrl":"10.1007/s41666-026-00246-8","url":null,"abstract":"<p><p>Systematic reviews are a key component of evidence-based medicine, playing a critical role in synthesizing existing research evidence and guiding clinical decisions. However, with the rapid growth of research publications, conducting systematic reviews has become increasingly burdensome, with title and abstract screening being one of the most time-consuming and resource-intensive steps. To mitigate this issue, we designed a two-stage dynamic few-shot learning (DFSL) approach aimed at improving the efficiency and performance of large language models (LLMs) in the title and abstract screening task. Specifically, this approach first uses a low-cost LLM for initial screening, then re-evaluates low-confidence instances using a high-performance LLM, thereby enhancing screening performance while controlling computational costs. We evaluated this approach across 10 systematic reviews, and the results demonstrate its strong generalizability and cost-effectiveness, with potential to reduce manual screening burden and accelerate the systematic review process in practical applications.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00246-8.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"654-665"},"PeriodicalIF":4.6,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852199","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}

{"title":"NeuroCypher ASD: Machine Learning with Knowledge Graph-Based Representation for ASD Screening in Toddlers.","authors":"Georgios Bouchouras, Dimitrios Doumanas, Konstantinos Kotis","doi":"10.1007/s41666-026-00243-x","DOIUrl":"10.1007/s41666-026-00243-x","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) screening often relies on structured questionnaires. Yet these data are not always easy to interpret or use in practice. There is a need for screening frameworks that support risk estimation, case comparison, and pattern exploration in a clear and accessible way. In this work, we present NeuroCypher ASD, a framework that combines tabular machine learning with a knowledge graph for ASD screening, interpretation, and data exploration. The tabular model uses questionnaire responses and demographic features to estimate ASD risk. The knowledge graph organizes the data and supports case comparison, pattern exploration, and natural language querying. The system is evaluated under a leakage-aware protocol using tabular, graph-based, and hybrid models. The results show strong performance for the tabular model and clarify the role of graph-based representations within the overall workflow. In this setting, the knowledge graph supports structured analysis of screening data by helping users examine relationships between cases, inspect shared features, and interpret predictions in context. To strengthen interpretability, the framework includes SHAP-based analysis and approximate mappings between embedding dimensions and original questionnaire features. It also includes an anomaly detection module that highlights atypical cases for closer review. Overall, NeuroCypher ASD provides a practical framework for combining prediction, interpretation, and data exploration in ASD screening. The results show the value of integrating machine learning, graph-based context, and natural language interaction in more transparent and accessible screening workflows, particularly in school-based environments.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"666-688"},"PeriodicalIF":4.6,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852201","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}

{"title":"From Promising Capabilities to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage.","authors":"Joseph Lee, Tianqi Shang, Jae Young Baik, Duy Duong-Tran, Shu Yang, Lingyao Li, Li Shen","doi":"10.1007/s41666-026-00241-z","DOIUrl":"10.1007/s41666-026-00241-z","url":null,"abstract":"<p><p>Large Language Models (LLMs) have shown significant promise for clinical applications, yet their application to triage remains underexplored. In this study, we systematically investigate the capabilities of LLMs in emergency department triage through two key dimensions: (1) robustness to distribution shifts and missing data, and (2) intersectional biases across sex and race. We assess multiple LLM-based approaches, ranging from continued pre-training to in-context learning, as well as conventional machine learning (ML) approaches. First, we demonstrate that LLMs exhibit superior robustness compared to traditional ML, which is promising due to their ability to provide explanatory rationales. Second, we show that the most effective LLM-based methods are those that select similar examples from prior patient cases, whereas reasoning capabilities in LLMs offer little benefit for triage. Lastly, we identify critical gaps in LLM preferences that emerge at the intersections of sex and race. LLMs exhibit sex-based differences, and they are more pronounced in certain racial groups, suggesting that LLMs encode preferences that emerge in specific clinical contexts and combinations of characteristics. We perform this audit through counterfactual analysis, providing a systematic way to identify such biases before real-world integration.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148715048","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}

{"title":"Semantic Knowledge Guided Diffusion Model for Biomedical Ontology Definition Generation.","authors":"Shailesh Dahal, Ratri Mukherjee, Nicholas Mathews, Bijaya Adhikari, Kishlay Jha","doi":"10.1007/s41666-026-00240-0","DOIUrl":"10.1007/s41666-026-00240-0","url":null,"abstract":"<p><p>Biomedical ontologies such as the Unified Medical Language System (UMLS) play a crucial role in supporting practical applications such as predicting gene-disease associations, drug-drug interactions, and biomedical question answering. However, automatically maintaining and updating them remains a critical barrier as authoring high-quality textual definitions is both time-consuming and labor-intensive. As a result, UMLS ontology updates significantly lag behind the pace of scientific discovery, leaving many concepts without definitions or with outdated descriptions. Despite the pressing need for automated generation of UMLS concept definitions, this problem has largely remained unaddressed in prior research. To address this, we propose a novel diffusion-based approach for generating high-quality definitions of biomedical concepts. A key innovation in our approach is the adaptive semantic modulation mechanism, which dynamically adjusts the influence of semantic priors throughout the denoising process, enabling the model to flexibly balance local lexical fidelity with global semantic alignment. Extensive experiments on the largest available biomedical ontology dataset demonstrate that the proposed approach both significantly and consistently outperforms strong baseline algorithms across multiple evaluation metrics.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"710-730"},"PeriodicalIF":4.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852242","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}

{"title":"MediHive: A Decentralized Agent Collective for Medical Reasoning.","authors":"Xiaoyang Wang, Christopher C Yang","doi":"10.1007/s41666-026-00239-7","DOIUrl":"10.1007/s41666-026-00239-7","url":null,"abstract":"<p><p>Large language models (LLMs) have revolutionized medical reasoning tasks, yet single-agent systems often falter on complex, interdisciplinary problems requiring robust handling of uncertainty and conflicting evidence. Multi-agent systems (MAS) leveraging LLMs enable collaborative intelligence, but prevailing centralized architectures suffer from scalability bottlenecks, single points of failure, and role confusion in resource-constrained environments. Decentralized MAS (D-MAS) promise enhanced autonomy and resilience via peer-to-peer interactions, but their application to high-stakes healthcare domains remains underexplored. We introduce MediHive, a novel decentralized multi-agent framework for medical question answering that integrates a shared memory pool with iterative fusion mechanisms. MediHive deploys LLM-based agents that autonomously self-assign specialized roles, conduct initial analyses, detect divergences through conditional evidence-based debates, and locally fuse peer insights over multiple rounds to achieve consensus. Empirically, MediHive outperforms single-LLM and centralized baselines on MedQA and PubMedQA datasets, attaining accuracies of 84.3% and 78.4%, respectively. Our work advances scalable, fault-tolerant D-MAS for medical AI, addressing key limitations of centralized designs while demonstrating superior performance in reasoning-intensive tasks.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"731-748"},"PeriodicalIF":4.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852151","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}

{"title":"Revealing the Latent Structure of Health Literacy in Online Peer-to-Peer Communication with Uncertainty-aware Bayesian Deep Learning.","authors":"Mouheb Mehdoui, Amel Fraisse, Mounir Zrigui, Widad Mustafa El Hadi","doi":"10.1007/s41666-026-00236-w","DOIUrl":"10.1007/s41666-026-00236-w","url":null,"abstract":"<p><p>Health literacy is a multidimensional construct essential for health decision-making, yet its computational assessment from naturalistic online discourse remains limited by categorical classifications that fail to capture its latent, continuous, and context-sensitive nature. To address this gap, we introduce an uncertainty-aware Bayesian deep learning framework that probabilistically infers latent health literacy from social media text while systematically quantifying predictive uncertainty. Using a large corpus of English-language health forum posts (<i>N</i> = 342 k), we operationalized five theoretical dimensions of health literacy-Functional, Communicative, Critical, Digital, and Expressed-through validated NLP features. A Bayesian Variational Autoencoder with Monte Carlo Dropout models health literacy as a continuous latent variable and provides epistemic uncertainty estimates. The framework recovers a robust three-factor latent struc- ture: Core Integrated Proficiency (merging Critical, Communicative, and Expressed dimensions), Digital Proficiency (exhibiting an inverse association with Functional Literacy), and Applied Functional Literacy. The model achieves strong reconstruction performance (MSE = 0.109) with uncertainty estimates reliably correlated to prediction error (r = 0.438). From the continuous latent representations, we derive three distinct user profiles-Balanced, Specialized, and Transitional-revealing heterogeneous patterns of health literacy expression and adaptive communication behavior. This work advances computational health literacy assessment by providing a probabilistic, uncertainty-aware framework that moves beyond static categorization, with direct implications for personalized public health communication and hybrid human-AI assessment systems.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"749-786"},"PeriodicalIF":4.6,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852260","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}

{"title":"Towards Social Good: How Can Social Media Inform the Next GenAI's Collaboration With Humans for Dietary Support?","authors":"Grace Ataguba, Oladapo Oyebode, Alaa Alslaity, George Frempong, Masud Imran, Rita Orji","doi":"10.1007/s41666-026-00235-x","DOIUrl":"10.1007/s41666-026-00235-x","url":null,"abstract":"<p><p>The rapid advancement of Generative AI (GenAI) has transformed human-computer interaction, particularly in domains requiring personalized guidance, such as nutritional planning. While GenAIs show promise in generating contextual nutritional recommendations, their integration into social interactions and behavioral patterns (which are key aspects of the Internet of Behavior - IoB) remains inadequately understood. This study investigates how social media discussions can inform the development of more effective human-centered GenAI systems for dietary support. Through a comprehensive analysis of 10,219 user-generated contents across Reddit, YouTube, and Bluesky social media platforms, we examine social interaction patterns, behavioral trends, and user experiences with GenAI-generated nutritional content. Our mixed-method approach combines computational analysis of big social data with qualitative assessment of user feedback, revealing crucial insights on the benefits and pitfalls of GenAIs, ranging from their contribution to users' health and wellness to their inconsistent outputs with human experts. Based on these insights, we recommend three conceptual frameworks that suggest the integration of GenAIs on social media with human experts' involvement and social media conversational engagements. Furthermore, our user study experiment involving one of the conceptual frameworks, FR3, revealed significant positive effects in users' attitude, dietary behavior intentions, and outcomes expectancy (<i>p</i><.001), alongside moderate trust in GenAIs. These findings contribute to the field through: (1) a structured framework for enhancing human-AI collaboration in nutritional guidance, (2) a validated machine learning model for sentiment classification in dietary AI discourse, and (3) empirical, qualitative insights, and evidence-based recommendations for integrating GenAIs for dietary guidance in social spaces. This research advances our understanding for developing socially responsive and human-centered GenAI systems that effectively promote sustainable healthy eating behaviors.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"10 3","pages":"787-849"},"PeriodicalIF":4.6,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852264","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}