Information (Switzerland)最新文献

{"title":"EHR Sampling Interval Bias Detection and Burden of Blood Pressure Excursions: Implications for Clinical Decision Support and Model Validity in Pediatric ECMO.","authors":"Neel Shah, Ethan Sanford, David R Busch, Ranveer Singh, Saurabh Mathur, Jayesh Sharma, Philip Reeder, Sriraam Natarajan, Lakshmi Raman","doi":"10.3390/info17020135","DOIUrl":"https://doi.org/10.3390/info17020135","url":null,"abstract":"<p><p>Routine Electronic Health Record (EHR) blood pressure charting under-samples dynamic physiology, risking missed hemodynamic instability. This study quantifies how HER-like down-sampling changes the detection and burden of hypo- and hypertension versus continuous monitoring and articulates the consequences for clinical decision support and machine learning label quality. We retrospectively analyzed 78 ECMO-supported pediatric patients (2019-2023). The continuous mean arterial pressure (MAP) captured every 5 s was resampled at intervals from 5 s to 1 h. We screened for 3 min windows of hypotension or hypertension at 10th/90th age-normed thresholds, comparing the per-patient event frequency and burden with EHR-derived recordings. At 10th/90th thresholds, hypotension events fell from 13,936 (5 s) to 3803 (15 min; -72.7%); the EHR captured 3471. Hypertension events dropped from 1573 to 410 (-73.9%); the EHR registered 1587. The EHR data overstated hypertension burden, indicating preferential documentation during prolonged instability while missing brief excursions. Standard EHR sampling significantly under-reports blood pressure derangements in pediatric ECMO. This underreporting of brief events may limit the accuracy of clinical decision support tools and machine learning algorithms in high-acuity patients. High-frequency data acquisition improves event capture and should be prioritized for clinical decision support and machine learning development.</p>","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"17 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13108679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784175","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":"Effect of Elevated Temperature on Physical Activity and Falls in Low-Income Older Adults Using Zero-Inflated Poisson and Graphical Models.","authors":"Tho Nguyen, Dahee Kim, Yingru Li, Christopher T Emrich, Jennifer Crook, Ladda Thiamwong, Rui Xie","doi":"10.3390/info16060442","DOIUrl":"10.3390/info16060442","url":null,"abstract":"<p><p>High ambient temperature poses a significant public health challenge, particularly for low-income older adults (LOAs) with preexisting health and social issues and disproportionate living conditions, placing them at a vulnerable condition of heat-related illnesses and associated public health risks. This study aims to utilize advanced statistical regression and machine learning methods to analyze complex relationships between elevated temperature, physical activity (PA), sociodemographic factors and fall incidents among LOAs. We collected data from a cohort of 304 LOAs aged 60 and above, living in free-living conditions in low-income communities in Central Florida, USA. Zero-inflated Poisson regression was employed to examine the linear relationships, which reflect the zero-abundant nature of fall incidents. Then, an advanced machine learning approach-the mixed undirected graphical model (MUGM)-was employed to further explore the intricate, nonlinear relationships among daily PA, daily temperature, and fall incidents. The findings suggest that more moderate-to-vigorous PA is significantly associated with fewer fall incidents (RR = 0.90, 95% CI: (0.816, 0.993), <i>p</i> = 0.037), after adjusting for other variables. In contrast, elevated temperature is strongly linked to a greater risk of falls (RR = 1.733, 95% CI: (1.581, 1.901), <i>p</i> < 0.0001), potentially reflecting seasonal influences. Although higher temperature increases fall events, this effect is mitigated among LOAs with increased sedentary behavior (<i>p</i> < 0.0001). Additionally, findings from the MUGM reinforce the intricate nature of falls. Fall counts were highly correlated with race and positively associated with temperature, highlighting the importance of tailoring fall prevention strategies to account for seasonal variations and health disparities, and promoting PA.</p>","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"16 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884001","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":"AI-Based Detection of Optical Microscopic Images of <i>Pseudomonas aeruginosa</i> in Planktonic and Biofilm States.","authors":"Bidisha Sengupta, Mousa Alrubayan, Manideep Kolla, Yibin Wang, Esther Mallet, Angel Torres, Ravyn Solis, Haifeng Wang, Prabhakar Pradhan","doi":"10.3390/info16040309","DOIUrl":"10.3390/info16040309","url":null,"abstract":"<p><p>Biofilms are resistant microbial cell aggregates that pose risks to the health and food industries and produce environmental contamination. The accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by <i>Pseudomonas aeruginosa</i> with high accuracy. Aptamer DNA-templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright-field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed.</p>","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"16 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12807543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999162","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":"Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder.","authors":"Ram Sapkota, Bishal Thapaliya, Bhaskar Ray, Pranav Suresh, Jingyu Liu","doi":"10.3390/info16030160","DOIUrl":"https://doi.org/10.3390/info16030160","url":null,"abstract":"<p><p>Today's advancements in neuroimaging have been pivotal in enhancing our understanding of brain development and function using various MRI techniques. This study utilizes images from T1-weighted imaging and diffusion-weighted imaging to identify gray matter and white matter coherent growth patterns within 2 years from 9-10-year-old participants in the Adolescent Brain Cognitive Development (ABCD) Study. The motivation behind this investigation lies in the need to comprehend the intricate processes of brain development during adolescence, a critical period characterized by significant cognitive maturation and behavioral change. While traditional methods like canonical correlation analysis (CCA) capture the linear interactions of brain regions, a deep canonical correlation analysis with an autoencoder (DCCAE) nonlinearly extracts brain patterns. The study involves a comparative analysis of changes in gray and white matter over two years, exploring their interrelation based on correlation scores, extracting significant features using both CCA and DCCAE methodologies, and finding an association between the extracted features with cognition and the Child Behavior Checklist. The results show that both CCA and DCCAE components identified similar brain regions associated with cognition and behavior, indicating that brain growth patterns over this two-year period are linear. The variance explained by CCA and DCCAE components for cognition and behavior suggests that brain growth patterns better account for cognitive maturation compared to behavioral changes. This research advances our understanding of neuroimaging analysis and provides valuable insights into the nuanced dynamics of brain development during adolescence.</p>","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"16 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11990250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986779","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":"Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.","authors":"Zina Ben-Miled, Jacob A Shebesh, Jing Su, Paul R Dexter, Randall W Grout, Malaz A Boustani","doi":"10.3390/info16010054","DOIUrl":"https://doi.org/10.3390/info16010054","url":null,"abstract":"<p><strong>Background: </strong>Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients. These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support.</p><p><strong>Objective: </strong>A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes.</p><p><strong>Design: </strong>A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis.</p><p><strong>Results: </strong>The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings.</p><p><strong>Conclusions: </strong>Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding.</p>","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"16 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972836","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":"Weakly Supervised Learning Approach for Implicit Aspect Extraction","authors":"Aye Aye Mar, Kiyoaki Shirai, Natthawut Kertkeidkachorn","doi":"10.3390/info14110612","DOIUrl":"https://doi.org/10.3390/info14110612","url":null,"abstract":"Aspect-based sentiment analysis (ABSA) is a process to extract an aspect of a product from a customer review and identify its polarity. Most previous studies of ABSA focused on explicit aspects, but implicit aspects have not yet been the subject of much attention. This paper proposes a novel weakly supervised method for implicit aspect extraction, which is a task to classify a sentence into a pre-defined implicit aspect category. A dataset labeled with implicit aspects is automatically constructed from unlabeled sentences as follows. First, explicit sentences are obtained by extracting explicit aspects from unlabeled sentences, while sentences that do not contain explicit aspects are preserved as candidates of implicit sentences. Second, clustering is performed to merge the explicit and implicit sentences that share the same aspect. Third, the aspect of the explicit sentence is assigned to the implicit sentences in the same cluster as the implicit aspect label. Then, the BERT model is fine-tuned for implicit aspect extraction using the constructed dataset. The results of the experiments show that our method achieves 82% and 84% accuracy for mobile phone and PC reviews, respectively, which are 20 and 21 percentage points higher than the baseline.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"58 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346763","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":"An Integrated Time Series Prediction Model Based on Empirical Mode Decomposition and Two Attention Mechanisms","authors":"Xianchang Wang, Siyu Dong, Rui Zhang","doi":"10.3390/info14110610","DOIUrl":"https://doi.org/10.3390/info14110610","url":null,"abstract":"In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequence. To accurately capture the regularities of subsequences using an attention mechanism, we propose an integrated model for time series prediction based on signal decomposition and two attention mechanisms. This model combines the results of three networks—LSTM, LSTM-self-attention, and LSTM-temporal attention—all trained using subsequences obtained from EMD. Additionally, since previous research on EMD has been limited to single series analysis, this paper includes multiple series by employing two data pre-processing methods: ‘overall normalization’ and ‘respective normalization’. Experimental results on various datasets demonstrate that compared to models without attention mechanisms, temporal attention improves the prediction accuracy of short- and medium-term decomposed series by 15~28% and 45~72%, respectively; furthermore, it reduces the overall prediction error by 10~17%. The integrated model with temporal attention achieves a reduction in error of approximately 0.3%, primarily when compared to models utilizing only general forms of attention mechanisms. Moreover, after normalizing multiple series separately, the predictive performance is equivalent to that achieved for individual series.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"24 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135087043","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":"Science Mapping of Meta-Analysis in Agricultural Science","authors":"Weiting Ding, Jialu Li, Heyang Ma, Yeru Wu, Hailong He","doi":"10.3390/info14110611","DOIUrl":"https://doi.org/10.3390/info14110611","url":null,"abstract":"As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&amp;F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"39 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135086865","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":"Polarizing Topics on Twitter in the 2022 United States Elections","authors":"Josip Katalinić, Ivan Dunđer, Sanja Seljan","doi":"10.3390/info14110609","DOIUrl":"https://doi.org/10.3390/info14110609","url":null,"abstract":"Politically polarizing issues are a growing concern around the world, creating divisions along ideological lines, which was also confirmed during the 2022 United States midterm elections. The purpose of this study was to explore the relationship between the results of the 2022 U.S. midterm elections and the topics that were covered during the campaign. A dataset consisting of 52,688 tweets in total was created by collecting tweets of senators, representatives and governors who participated in the elections one month before the start of the elections. Using unsupervised machine learning, topic modeling is built on the collected data and visualized to represent topics. Furthermore, supervised machine learning is used to classify tweets to the corresponding political party, whereas sentiment analysis is carried out in order to detect polarity and subjectivity. Tweets from participating politicians, U.S. states and involved parties were found to correlate with polarizing topics. This study hereby explored the relationship between the topics that were creating a divide between Democrats and Republicans during their campaign and the 2022 U.S. midterm election outcomes. This research found that polarizing topics permeated the Twitter (today known as X) campaign, and that all elections were classified as highly subjective. In the Senate and House elections, this classification analysis showed significant misclassification rates of 21.37% and 24.15%, respectively, indicating that Republican tweets often aligned with traditional Democratic narratives.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":" 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191850","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":"Context-Aware Personalization: A Systems Engineering Framework","authors":"Olurotimi Oguntola, Steven Simske","doi":"10.3390/info14110608","DOIUrl":"https://doi.org/10.3390/info14110608","url":null,"abstract":"This study proposes a framework for a systems engineering-based approach to context-aware personalization, which is applied to e-commerce through the understanding and modeling of user behavior from their interactions with sales channels and media. The framework is practical and built on systems engineering principles. It combines three conceptual components to produce signals that provide content relevant to the users based on their behavior, thus enhancing their experience. These components are the ‘recognition and knowledge’ of the users and their behavior (persona); the awareness of users’ current contexts; and the comprehension of their situation and projection of their future status (intent prediction). The persona generator is implemented by leveraging an unsupervised machine learning algorithm to assign users into cohorts and learn cohort behavior while preserving their privacy in an ethical framework. The component of the users’ current context is fulfilled as a microservice that adopts novel e-commerce data interpretations. The best result of 97.3% accuracy for the intent prediction component was obtained by tokenizing categorical features with a pre-trained BERT (bidirectional encoder representations from transformers) model and passing these, as the contextual embedding input, to an LSTM (long short-term memory) neural network. Paired cohort-directed prescriptive action is generated from learned behavior as a recommended alternative to users’ shopping steps. The practical implementation of this e-commerce personalization framework is demonstrated in this study through the empirical evaluation of experimental results.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":" 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135190888","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}