Biodata Mining最新文献

{"title":"An unsupervised tool for biomarker discovery and cancer subtyping applied to glioblastoma.","authors":"Roberta Coletti, J Orestes Cerdeira, Marcos Raydan, Marta B Lopes","doi":"10.1186/s13040-025-00500-6","DOIUrl":"10.1186/s13040-025-00500-6","url":null,"abstract":"<p><strong>Background: </strong>High-dimensional omics data often contain more variables than observations, which can lead to overfitting and negatively impact the results of classical data analysis methods. To address the issue, supervised variable selection methods are often used, incorporating penalty terms into the model. While effective for selecting task-specific variables, this approach may not preserve the overall dataset structure for multiple downstream analyses. This study aims to evaluate unsupervised variable selection approaches and introduce a novel tool that improves data interpretability while maintaining biological information.</p><p><strong>Results: </strong>We assessed multiple unsupervised variable selection techniques to identify a representative subset of the original dataset. Based on this evaluation, we developed TRIM-IT, a computational tool that integrates unsupervised variable selection, clustering, survival analysis, and differential gene expression analysis. TRIM-IT was applied to glioblastoma transcriptomics data, uncovering three distinct patient clusters. These clusters correlated with tumor histology, exhibited significantly different survival outcomes, and revealed molecular profiles that suggest potential biomarker candidates.</p><p><strong>Conclusion: </strong>TRIM-IT provides a novel approach for analyzing high-dimensional omics data while preserving key biological insights. Its ability to identify meaningful patient subgroups and molecular signatures highlights its applicability across various biomedical research contexts. The tool is implemented in R and the code is publicly available for reproduction and adaptation to other studies.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"85"},"PeriodicalIF":7.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12720448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145811993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.","authors":"Su-Yin Hsu, Jhe-Yi Jhu, Jun-Wan Gao, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu","doi":"10.1186/s13040-025-00509-x","DOIUrl":"10.1186/s13040-025-00509-x","url":null,"abstract":"<p><strong>Background: </strong>Emergency Department (ED) revisits represent a critical issue in emergency medicine. Identifying high-risk revisit cases (revisits with intensive care unit admissions, cardiac arrest, or requiring emergency surgery) is particularly important. While prior studies have explored machine learning models for ED revisit prediction, few deep learning approaches exist, and dynamic features remain underutilized.</p><p><strong>Methods: </strong>We used data from National Taiwan University Hospital (NTUH), incorporating both static (e.g., age, sex, triage) and dynamic (vital signs) features. A preprocessing strategy was developed to handle temporal irregularities. We proposed a hybrid deep learning model combining Temporal Convolutional Network (TCN) and feature tokenizer (FT)-Transformer to integrate static and short-term dynamic information.</p><p><strong>Results: </strong>We evaluated our model on NTUH 2016-2019 data, achieving the area under the receiver operating characteristic curve (AUROC) of 0.8453 and the area under precision recall curve (AUPRC) of 0.0935 for high-risk revisits (base rate = 0.01), and AUROC of 0.7250 and AUPRC of 0.2005 for general revisits (base rate = 0.042). The model maintained robust performance when validated on 2020-2022 data. Compared to the static-only logistic regression baseline, our model improved AUPRC from 0.0288 to 0.0935 and precision from 0.0281 to 0.0428.</p><p><strong>Conclusion: </strong>Our model significantly outperformed the static-only baseline. It demonstrates the effectiveness of multimodal clinical data fusion in improving ED revisit prediction and supporting clinical decision-making.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"88"},"PeriodicalIF":6.1,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12750547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145800645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven SaO₂ prediction from pulse oximetry and electronic health records.","authors":"JiWon Woo, Orian Stapleton, Jay Luo, Chao Cheng Chuang, Yolanda Su, Sreenidhi Sankararaman, Esanika Mukherjee, Nikita Sivakumar, Katherine Calligy, Summer Duffy, Rebecca Mosier, Joseph Greenstein, Casey Overby Taylor, Danielle Gottlieb Sen","doi":"10.1186/s13040-025-00511-3","DOIUrl":"10.1186/s13040-025-00511-3","url":null,"abstract":"<p><p>OBJECTIVE: To address limitations in pulse oximetry accuracy associated with low saturation and melanin content using machine learning (ML) and to compare our results to standard pulse oximetry and gold standard arterial blood gas readings. BACKGROUND: Oxygen saturation is traditionally measured through the gold standard arterial blood gas (SaO2) or pulse oximetry (SpO2), which approximates SaO2 using light absorption patterns. However, SpO2 has been shown to overestimate oxygen saturation, particularly in individuals with darker skin tone, leading to hidden hypoxemia and delayed medical interventions. METHOD: We developed a machine learning (ML) model trained on the BOLD dataset, integrating patient data from eICU, MIMIC-III, and MIMIC-IV (n = 49,093). With 64 clinical features and 2 outcomes (SaO2 and hidden hypoxemia events), we trained regression ML models (linear regression, random forest, and XGBoost) to predict SaO2, minimizing the mean squared error between predicted SaO2 and ground truth SaO2. We used our test data to compare model performance to standard SaO2 measurement with accuracy root mean square error (Arms), R2, and change in hidden hypoxemia events. We used SHapley Additive exPlanations (SHAP) to rank important features for SaO2 prediction. RESULTS: The XGBoost-Vanilla model improved Arms to 3.3% from a baseline of 4.1%. In low SaO2 patients (SaO2 < 90%), the accuracy of pulse oximetry was heavily compromised with Arms of 10.3%; the linear regression model with weighted loss was able to reduce Arms to 8.6%. We found that SpO2, creatinine level, mean corpuscular hemoglobin level, respiratory rate, and age at admission were the leading features driving the SaO2 prediction. CONCLUSION: These findings suggest that ML-based models can enhance the accuracy of the standard SpO2. Further investigation is warranted to address SpO₂ inaccuracies and bias. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"90"},"PeriodicalIF":7.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12752230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145800264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subphenotype heterogeneity to guide predictive enrichment in acute kidney injury: insights from machine learning and target trial emulation.","authors":"Jiayang Li, Mingyi Zhao, Qingnan He","doi":"10.1186/s13040-025-00514-0","DOIUrl":"10.1186/s13040-025-00514-0","url":null,"abstract":"<p><p>BACKGROUND: Acute kidney injury (AKI) exhibits substantial heterogeneity in clinical presentation. Previous studies used traditional clustering approaches to identify subphenotypes, often focusing on adverse clinical outcomes, while ignoring that the central goal of subphenotyping is to enable individualized care. OBJECTIVE: In this study, we aimed to identify distinct AKI subphenotypes and evaluate their heterogeneous associations with vasopressor choice and renal replacement therapy (RRT) strategies. METHODS: This retrospective cohort study used data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and included 31,030 patients diagnosed with AKI within 48 h of admission. Generative topographic mapping, a probabilistic unsupervised machine-learning model, was used to identify clusters. We then applied a target trial emulation framework to emulate comparisons of norepinephrine versus vasopressin, RRT modalities, and continuous RRT initiation timing across subphenotypes. RESULTS: Four distinct subphenotypes were identified, with marked differences in clinical features, laboratory abnormalities, and outcomes: a large group with intermediate severity; a hyper-inflammatory subphenotype with marked liver dysfunction and severe AKI; a cardiorenal congestion subphenotype with high cardiovascular comorbidity; and a younger subphenotype enriched for postoperative or trauma patients. Vasopressin was associated with reduced mortality in Subphenotype 2. A more delayed continuous RRT initiation strategy was linked to lower 60-day mortality in Subphenotypes 1 and 2. These associations remained robust in sensitivity analyses. CONCLUSIONS: We identified four clinically distinct AKI subphenotypes that demonstrated substantial heterogeneity in their mortality associations with vasopressor use and RRT strategies. These findings could improve prognostication and advance precision medicine in critical care. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"87"},"PeriodicalIF":7.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12752266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145800628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study.","authors":"Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K Triedman, Sunil J Ghelani, William G La Cava","doi":"10.1186/s13040-025-00510-4","DOIUrl":"10.1186/s13040-025-00510-4","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence applied to electrocardiography (AI-ECG) has recently shown potential for mortality prediction, but heterogeneous approaches and private datasets have limited generalizable insights into AI methodologies fit for this purpose. To address this, we systematically evaluated model design choices across three large medical center cohorts: Beth Isreal Deaconess (MIMIC-IV: n = 795,546 ECGs, United States), Telehealth Network of Minas Gerais (Code-15; n = 345,779, Brazil), and Boston Children's Hospital (BCH; n = 255,379, United States).</p><p><strong>Results: </strong>We comprehensively evaluates models to predict all-cause mortality, comparing horizon-based classification and deep survival methods various neural architectures including convolutional neural networks and transformers. We also benchmarked against demographic-only and gradient boosting baselines. Top models yielded good performance (median concordance, Code-15: 0.83; MIMIC-IV: 0.78; BCH: 0.81). Incorporating age and sex improved performance across all datasets. Classifier-Cox models exhibited site-dependent sensitivity to horizon choice (median Pearson's R, Code-15: 0.35; MIMIC-IV: -0.71; BCH: 0.37). External validation reduced concordance, and in some cases, demographic-only models outperformed externally trained AI-ECG models on Code-15. However, models trained on multi-site data outperformed site-specific models by margins ranging from 5% to 22%.</p><p><strong>Conclusions: </strong>These findings highlight several key factors for robust AI-ECG deployment. Deep survival methods consistently provided advantages over horizon-based classifiers, while inclusion of demographic covariates such as age and sex improved predictive performance across sites. The sensitivity of classifier-based models to horizon selection underscores the need for site-specific calibration. The multi-site experiment reveals that cross-cohort training, even between adult and pediatric cohorts, can substantially improve performance on those cohorts compared to cohort-specific training. Together, these results emphasize the importance of model type, demographic features, and training data diversity in developing AI-ECG models that can be reliably applied across populations.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"6"},"PeriodicalIF":6.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12821881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CatBoost with physics-based metaheuristics for thyroid cancer recurrence prediction.","authors":"Proshenjit Sarker, Kwonhue Choi, Abdullah-Al Nahid, Md Abdus Samad","doi":"10.1186/s13040-025-00494-1","DOIUrl":"10.1186/s13040-025-00494-1","url":null,"abstract":"<p><p>Thyroid Cancer (TC) is the uncontrolled growth of carcinogenic cells in the thyroid gland, with a higher recurrence rate than other cancers. Early detection of TC recurrence (TCR) is crucial for timely intervention. This study develops machine-learning algorithms that reduce features while maintaining high performance. Previous studies on the Differentiated Thyroid Cancer Recurrence (DTCR) dataset struggled to improve performance with feature reduction, and misclassification causes remained unexplored. This work proposes three Physics-based Metaheuristic Algorithms (PBMHAs)—Energy Valley Optimization (EVOA), Equilibrium Optimization (EOA), and Electromagnetic Field Optimization (EFOA)—combined with the Categorical Boosting (CatBoost) classifier. SHAP is used to analyze feature importance. CatBoost without optimization (Only CB) achieved 95.83% Accuracy, 92.42% F-score, 96.29% Precision, and 89.27% Recall using all 16 features. After optimization, EVOA_CB reached 96.35% mean accuracy, while EOA_CB and EFOA_CB achieved 96.17%. EOA_CB excluded 11 less important features, and EFOA_CB attained the highest mean AUC of 0.994 with the lowest computational times. Additionally, this work provides insights into the factors contributing to misclassification. Using a 30:70 train-test split over 5 folds, EVOA_CB performed best on six selected features, with 96.35% Accuracy, 93.34% F-score, and 96.19% Precision. SHAP highlighted response, risk, and N as the most important features. These findings support early, efficient detection of TC recurrence with fewer features.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"84"},"PeriodicalIF":7.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12687482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing large language models for ontology-based annotation: a study on gene ontology in biomedical texts.","authors":"Pratik Devkota, Somya D Mohanty, Prashanti Manda","doi":"10.1186/s13040-025-00507-z","DOIUrl":"10.1186/s13040-025-00507-z","url":null,"abstract":"<p><p>Automated ontology annotation of scientific literature plays a critical role in knowledge management, particularly in fields like biology and biomedicine, where accurate concept tagging can enhance information retrieval, semantic search, and knowledge integration. Traditional models for ontology annotation, such as Recurrent Neural Networks (RNNs) and Bidirectional Gated Recurrent Units (Bi-GRUs), have been effective but limited in handling complex biomedical terminologies and semantic nuances. This study explores the potential of large language models (LLMs), including MPT-7B, Phi, BiomedLM, and Meditron, for improving ontology annotation, specifically with Gene Ontology (GO) concepts. We fine-tuned these models on the CRAFT dataset, assessing their performance in terms of F1 score, semantic similarity, memory usage, and inference speed. Our results show that while Bi-GRU baselines remain competitive in raw accuracy, LLMs offer complementary strengths. LLMs exhibit qualitatively higher semantic consistency in some cases, particularly when handling complex or multi-word ontology terms. However, these observations are exploratory and not statistically verified across all model types. However, resource requirements for LLMs are notably high, raising considerations about computational efficiency. Techniques like parameter-efficient fine-tuning (PEFT) and advanced prompting were explored to address these challenges, demonstrating potential in reducing computational demands while maintaining performance. Our findings suggest that while LLMs offer advantages in annotation accuracy, practical deployment should balance these benefits with resource costs. This research highlights the need for further optimization and domain-specific training to make LLMs a feasible choice for real-world biomedical ontology annotation tasks.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"5"},"PeriodicalIF":6.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12781589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinically aligned multi-modal image-text model for pan-cancer prognosis prediction.","authors":"Jonghyun Lee, Jacob S Leiby, Lina Takemaru, Yidi Huang, Myung-Giun Noh, Jaesik Kim, Byonghan Lee, Mattew E Lee, Derek A Oldridge, Young-Gyu Eun, Hyun Jee Lee, Young Chan Lee, Dokyoon Kim","doi":"10.1186/s13040-025-00495-0","DOIUrl":"10.1186/s13040-025-00495-0","url":null,"abstract":"<p><p>Cancer prognosis prediction increasingly leverages multi-modal learning, yet existing approaches often rely on omics data that are costly and difficult to collect in routine practice. Pathology reports, by contrast, are routinely generated and widely available, providing a practical textual modality complementary to histology. We present CALM (Clinical Anchor text guided Learning for Multi-modal prognosis prediction), a general framework that integrates pathology images and reports through risk-specific anchor texts. CALM systematically incorporates prior clinical knowledge by guiding image and text alignment with large language model–derived anchors, further refined via few-shot tuning. Across 14 TCGA cancer types, CALM improved prognostic accuracy compared to image and text baselines (up to +11.5% mean C-index), with enhanced training stability. CALM achieved performance comparable to image–omics integration in PORPOISE (0.652 vs. 0.644), highlighting the prognostic value of text. External validation in an independent head and neck cancer cohort demonstrated that CALM improved generalization in zero-shot settings. Attention-based interpretation further confirmed that CALM aligns diagnostic text with tumor regions. Together, CALM offers a simple, interpretable, and clinically viable strategy for prognosis prediction, expanding the role of routine pathology reports as scalable priors in multi-modal oncology AI.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"4"},"PeriodicalIF":7.9,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12781818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145696548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fairness-aware machine learning framework for maternal health in Ghana: integrating explainability, bias mitigation, and causal inference for ethical AI deployment.","authors":"Augustus Osborne, Kobloobase Usani","doi":"10.1186/s13040-025-00505-1","DOIUrl":"10.1186/s13040-025-00505-1","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BACKGROUND: Antenatal care (ANC) uptake in Ghana remains inequitable, with socioeconomic and geographic disparities limiting progress toward universal maternal health coverage (SDG 3). We present a novel, fairness-aware machine learning framework for predicting antenatal care uptake among women in Ghana, integrating explainability, bias mitigation, and causal inference to support ethical artificial intelligence (AI) deployment in low- and middle-income countries. METHODS: Using the 2022 Ghana Demographic and Health Survey (n = 3,314 eligible women with a recent live birth), we applied multiple imputation by chained equations (m = 10), appropriate categorical encoding, and synthetic minority oversampling (SMOTE) within training folds. Four supervised models (logistic regression, random forest, XGBoost, support vector machine) underwent stratified 5‑fold nested cross‑validation with cost‑sensitive threshold optimization (selected probability threshold = 0.45). Explainability (SHAP), fairness auditing (AIF360; metrics: statistical parity difference, disparate impact, equal opportunity difference, average odds difference, theil index), preprocessing mitigation (reweighing), counterfactual explanations (DiCE), and cautious treatment effect estimation (causal forests within a double machine learning framework) were integrated. Performance metrics included accuracy, precision, recall, F1, ROC‑AUC, minority class PR‑AUC, balanced accuracy, calibration (Brier score), and decision curve net benefit. RESULTS: The optimized random forest model achieved the highest accuracy (0.68) and recall (0.84) in identifying women with inadequate ANC contacts. Calibration was strong, with a brier score of 0.158, a calibration slope of 0.97, and an intercept of − 0.02. Fairness auditing revealed baseline disparities in model predictions across wealth, region, ethnicity, and religion, with a statistical parity difference for wealth status of 0.182 and a Disparate Impact of 1.62. Following reweighting, disparate impact improved into the fairness range (0.92; within the recommended 0.8–1.25 interval), and statistical parity difference reduced to − 0.028. Counterfactual analysis indicated that education, wealth, media exposure, and health worker contacts were the most modifiable factors for improving ANC uptake. Exploratory causal inference using double machine learning suggested that improving wealth status and education could be associated with a 16% (Average Treatment Effect [ATE] = 0.163) and 14% (ATE = 0.142) increase, respectively, in the probability of adequate ANC, with greater effects observed among urban and educated subgroups. Adjusted odds ratio (AOR) analysis showed that women in the richest quintile were nearly twice as likely to receive adequate ANC (AOR = 1.91, 95% CI: 1.44–2.53; p &lt; 0.001), while those in the poorest quintile had significantly lower odds (AOR = 0.58, 95% CI: 0.45–0.75; p &lt; 0.001). Additional significant predictors included health insura","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"3"},"PeriodicalIF":7.9,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12781815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145688450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radio Frequency Tagging-enabled patient monitoring: integrating mobility tracking with early warning systems for enhanced safety.","authors":"Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi","doi":"10.1186/s13040-025-00504-2","DOIUrl":"10.1186/s13040-025-00504-2","url":null,"abstract":"<p><p>Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive health-care interventions. The proposed system uses a spatio-temporal probabilistic network to learn typical movement patterns from historical tagging data and applies a dynamic similarity analysis to detect deviations in real time. Anomaly scores generated from these comparisons are combined with clinical indicators to produce automated alerts for healthcare providers, supporting timely and informed responses. The framework is evaluated on a hybrid dataset comprising simulated tagging traces and publicly available physiological and activity data, including measurements of heart rate, respiration, and physical movement. The Dynamic Time Warping-based anomaly detection system achieved consistently high performance across all patient categories, with accuracy ranging from 87.5% to 91.0%, sensitivity between 88.4% and 96.2%, and F1-scores up to 93.5%, demonstrating its strong capability to effectively distinguish between normal and abnormal movement patterns across diverse clinical conditions. By combining location-based surveillance with predictive modelling and clinical scoring, the framework offers a context-aware and reliable tool for continuous patient monitoring, thereby enhancing safety and supporting data-driven clinical decision-making.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"2"},"PeriodicalIF":7.9,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12781683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}