BMC Medical Informatics and Decision Making最新文献

{"title":"The FAIR data point populator: collaborative FAIRification and population of FAIR data points.","authors":"Daphne Wijnbergen, Rajaram Kaliyaperumal, Kees Burger, Luiz Olavo Bonino da Silva Santos, Barend Mons, Marco Roos, Eleni Mina","doi":"10.1186/s12911-025-03022-7","DOIUrl":"10.1186/s12911-025-03022-7","url":null,"abstract":"<p><strong>Background: </strong>Use of the FAIR principles (Findable, Accessible, Interoperable and Reusable) allows the rapidly growing number of biomedical datasets to be optimally (re)used. An important aspect of the FAIR principles is metadata. The FAIR Data Point specifications and reference implementation have been designed as an example on how to publish metadata according to the FAIR principles. Metadata can be added to a FAIR Data Point with the FDP's web interface or through its API. However, these methods are either limited in scalability or only usable by users with a background in programming. We aim to provide a new tool for populating FDPs with metadata that addresses these limitations with the FAIR Data Point Populator.</p><p><strong>Results: </strong>The FAIR Data Point Populator consists of a GitHub workflow together with Excel templates that have tooltips, validation and documentation. The Excel templates are targeted towards non-technical users, and can be used collaboratively in online spreadsheet software. A more technical user then uses the GitHub workflow to read multiple entries in the Excel sheets, and transform it into machine readable metadata. This metadata is then automatically uploaded to a connected FAIR Data Point. We applied the FAIR Data Point Populator on the metadata of two datasets, and a patient registry. We were then able to run a query on the FAIR Data Point Index, in order to retrieve one of the datasets.</p><p><strong>Conclusion: </strong>The FAIR Data Point Populator addresses the limitations of the other metadata publication methods by allowing the bulk creation of metadata entries while remaining accessible for users without a background in programming. Additionally, it allows efficient collaboration. As a result of this, the barrier of entry for FAIRification is lower, which allows the creation of FAIR data by more people.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 Suppl 1","pages":"211"},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.","authors":"Yang Li, Kun Zou, Yixuan Wang, Yucheng Zhang, Jingtao Zhong, Wu Zhou, Fang Tang, Lu Peng, Xusheng Liu, Lili Deng","doi":"10.1186/s12911-025-03043-2","DOIUrl":"10.1186/s12911-025-03043-2","url":null,"abstract":"<p><p>The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML) technology has substantial potential to disease prediction. The present study use dataset from the Chinese Health and Retirement Longitudinal Study (CHARLS) and utilizes advanced Gradient Boosting algorithms to develop predictive models. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the key predictors, and multivariate logistic regression was utilized to validate the independent predictive power of the variables. Furthermore, the study integrated SHapley Additive exPlanations (SHAP) to boost the interpretability of the model. The findings show that the Gradient Boosting Model demonstrated robust performance across both the training and test datasets. Specifically, it attained AUC values of 0.8 and 0.765 in the training and test sets, respectively, while achieving accuracy scores of 0.736 and 0.728 in these two datasets. LASSO regression identified key influencing factors, including estimated glomerular filtration rate (eGFR), age, hemoglobin (Hb), glucose, and systolic blood pressure (SBP). Multivariate linear regression further confirmed the independent associations between these variables and rapid kidney function deterioration (P < 0.05). This study developed a risk assessment model for rapid kidney function deterioration that is applicable to middle-aged and elderly populations in China.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"210"},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12144772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.","authors":"Qiong Ma, Runqi Meng, Ruiting Li, Ling Dai, Fu Shen, Jie Yuan, Danqi Sun, Manman Li, Caixia Fu, Rong Li, Feng Feng, Yonggang Li, Tong Tong, Yajia Gu, Yiqun Sun, Dinggang Shen","doi":"10.1186/s12911-025-03050-3","DOIUrl":"10.1186/s12911-025-03050-3","url":null,"abstract":"<p><strong>Background: </strong>Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.</p><p><strong>Methods: </strong>This retrospective study enrolled 321 rectal cancer patients (training set: 212; internal testing set: 53; external testing set: 56) who directly received total mesorectal excision from five hospitals between March 2014 to April 2021. A multitask deep learning model was developed to simultaneously predict recurrence/metastasis and disease-free survival (DFS). The model integrated clinicopathologic data and multiparametric magnetic resonance imaging (MRI) images including diffusion kurtosis imaging (DKI), without performing tumor segmentation. The receiver operating characteristic (ROC) curve and Harrell's concordance index (C-index) were used to evaluate the predictive performance of the proposed model.</p><p><strong>Results: </strong>The deep learning model achieved good discrimination capability of recurrence/metastasis, with area under the curve (AUC) values of 0.885, 0.846, and 0.797 in the training, internal testing and external testing sets, respectively. Furthermore, the model successfully predicted DFS in the training set (C-index: 0.812), internal testing set (C-index: 0.794), and external testing set (C-index: 0.733), and classified patients into significantly distinct high- and low-risk groups (p < 0.05).</p><p><strong>Conclusions: </strong>The multitask deep learning model, incorporating clinicopathologic data and multiparametric MRI, effectively predicted both recurrence/metastasis and survival for patients with rectal cancer. It has the potential to be an essential tool for risk stratification, and assist in making individualized treatment decisions.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"209"},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models.","authors":"Yawei Yang, Junjie Tang, Liping Ma, Feng Wu, Xiaoqing Guan","doi":"10.1186/s12911-025-03052-1","DOIUrl":"10.1186/s12911-025-03052-1","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and objective: &lt;/strong&gt;The machine learning (ML) models for acute myocardial infarction (AMI) are considered to have better predictive ability for mortality compared to conventional risk scoring models. However, previous ML prediction models have mostly been short-term (1 year or less) models. Here, we established ML models for long-term prediction of AMI mortality (5 years or 10 years) and systematically compare the predictive capabilities of short-term models versus long-term models across varying survival time periods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;An observational retrospective study was conducted to analyse mortality prediction in patients with varying survival times. A total of 4,173 patients were enrolled from two different hospitals in China. The dataset was allocated into three groups and an external test set based on their survival duration: the 1-year group (n = 3,626), the 5-year group (n = 2,102), the 10-year group (n = 721), and the external test set (n = 545). A comprehensive set of 53 variables was collected and utilized for model development. Mortality prediction was analysed using oversampling and feature selection methods coupled with machine learning algorithms. SHapley Additive exPlanations (SHAP) values were utilized to quantify the feature importance of AMI risk. The best-performing models from each group were selected for a systematic comparison of predictive accuracy using the external test set with follow-up exceeding 10 years but with varying survival times.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For the 1-year model, the RF model achieved the best performance on the test dataset, with an F1 score of 97.81% using only oversampling without feature selection. Conversely, in the case of the 5-years, the combination of LASSO and RF yielded the best performance, achieving F1 scores of 91.35% with both feature selection and oversampling. The best model of 10-years group was SVM with only oversampling without feature selection, yielding an F1 score of 80.7%. Age, BNP, and the Killip classification of AMI were consistently identified as robust predictors across all three groups. This underscores aging as a critical AMI risk factor contributing to mortality. However, despite the model's success, when examining the actual survival times of the 545 patients, of which 64% survived beyond 5 years and 37% beyond 10 years, the 1-year model failed to distinguish between these patients, predicting all as low risk. This highlights the limitation of short-term models, indicating their inability to accurately predict actual long-term survival times despite being commonly used in AMI mortality prediction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The study identifies Age, BNP, and Killip classification as consistent predictors of AMI mortality across all groups, with Age being the most significant factor. CBC parameters and renal biomarkers were pivotal in short-term models, while therapeutic interventions gained prominence over","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"208"},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning model applied to real-time delineation of colorectal polyps.","authors":"Moana Gelu-Simeon, Adel Mamou, Georgette Saint-Georges, Marceline Alexis, Marie Sautereau, Yassine Mamou, Jimmy Simeon","doi":"10.1186/s12911-025-03047-y","DOIUrl":"10.1186/s12911-025-03047-y","url":null,"abstract":"<p><strong>Background: </strong>Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its application in medical settings remains underexplored. This study evaluated the performance of a YOLACT-derived Real-time Polyp Delineation Model (RTPoDeMo) for real-time use on prospectively recorded colonoscopy videos.</p><p><strong>Methods: </strong>Twelve combinations of architectures, including Mask-RCNN, YOLACT, and YOLACT++, paired with backbones such as ResNet50, ResNet101, and DarkNet53, were tested on 2,188 colonoscopy images with three image resolution sizes. Dataset preparation involved pre-processing and segmentation annotation, with optimized image augmentation.</p><p><strong>Results: </strong>RTPoDeMo, using YOLACT-ResNet50, achieved 72.3 mAP and 32.8 FPS for real-time instance segmentation based on COCO annotations. The model performed with a per-image accuracy of 99.59% (95% CI: [99.45 - 99.71%]), sensitivity of 90.63% (95% CI: [78.95 - 93.64%]), specificity of 99.95% (95% CI: [99.93 - 99.97%]) and a F1-score of 0.94 (95% CI: [0.87-0.98]). In validation, out of 36 polyps detected by experts, RTPoDeMo missed only one polyp, compared to six missed by senior endoscopists. The model demonstrated good agreement with experts, reflected by a Cohen's Kappa coefficient of 0.72 (95% CI: [0.54-1.00], p < 0.0001).</p><p><strong>Conclusions: </strong>Our model provides new perspectives in the adaptation of YOLACT to the real-time delineation of colorectal polyps. In the future, it could improve the characterization of polyps to be resected during colonoscopy.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"206"},"PeriodicalIF":3.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing blood cell detection and classification: performance evaluation of modern deep learning models.","authors":"Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene","doi":"10.1186/s12911-025-03027-2","DOIUrl":"10.1186/s12911-025-03027-2","url":null,"abstract":"<p><p>The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification has a high clinical relevance in these patients because this would help to prevent false-negative diagnosis and to treat them in a timely and effective manner, thus reducing their clinical impacts.Our research aims to automate the process and eliminate manual efforts in blood cell counting. While our primary focus is on detection and classification, the output generated by our approach can be useful for disease prediction. This follows a two-step approach, where YOLO-based detection is first performed to locate blood cells, followed by classification using a hybrid CNN model to ensure accurate identification. We conducted a thorough and extensive comparison with other state-of-the-art models, including MobileNetV2, ShuffleNetV2, and DarkNet, for blood cell detection and classification. In terms of real-time performance, YOLOv10 outperforms other object detection models with better detection rates and classification accuracy. But MobileNetV2 and ShuffleNetV2 are more computationally efficient, which becomes more appropriate for resource-constrained environments. In contrast, DarkNet outperformed in terms of feature extraction performance, and the fine blood cell type classification. Additionally, an annotated blood cell data set was generated for this study. A diverse set of blood cell images with fine-grained annotations is contained in this dataset to make it useful for deep learning models training and evaluation. Because the present dataset will be an important resource for researchers and developers working on automatic blood cell detection and classification systems, we will make it publicly available under the open-access nature in order to accelerate the collaboration and progress in this field.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"207"},"PeriodicalIF":3.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.","authors":"Peter Hill, Daniel Jonsson, Jakob Lederman, Peter Bolin, Veronica Vicente","doi":"10.1186/s12911-025-03046-z","DOIUrl":"10.1186/s12911-025-03046-z","url":null,"abstract":"<p><strong>Background: </strong>Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently understood. In this exploratory study, we applied machine learning (ML) to examine how emergency medical response time, age, and sex jointly influence the probability of encountering HRTS conditions.</p><p><strong>Methods: </strong>A retrospective observational analysis was conducted on 132,395 prehospital missions in Stockholm (2017-2022). Multiple ML models, random forest, gradient boosting, neural networks, and logistic regression were trained to probe potential nonlinear patterns and interactions, not with the primary goal of predictive accuracy. Model performance was evaluated using sensitivity, specificity, and area under the curve (AUC) measures. However, partial dependence (PD) and individual conditional expectation (ICE) plots were the principal tools illustrating how response time, age, and sex shape HRTS likelihood.</p><p><strong>Results: </strong>PD and ICE plots revealed that older age (> 60 years) was consistently associated with a higher probability of HRTS. Moreover, patients over 60 years displayed a complex, rising risk at prolonged response times exceeding two hours. Gradient boosting offered the best (though modest) classification metrics, with an AUC of 0.66 and an F1-score of 0.55. We emphasize that these metrics, while necessary for completeness, were secondary to our aim of characterizing nonlinear relationships.</p><p><strong>Conclusions: </strong>Our findings underscore the exploratory value of ML in identifying subtle relationships and interactions among response time, age, and sex for time-sensitive breathing emergencies. These results highlight opportunities to refine dispatch protocols, develop age- and sex-focused screening questions, and revisit lower-priority calls after extended wait times. Future work should incorporate richer data and refine these insights for potential predictive use.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"205"},"PeriodicalIF":3.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.","authors":"Yusuke Kobayashi, Naoki Fujiwara, Yuki Murakami, Shoichi Ishida, Sho Kinguchi, Tatsuya Haze, Kengo Azushima, Akira Fujiwara, Hiromichi Wakui, Masayoshi Sakakura, Kei Terayama, Nobuhito Hirawa, Tetsuo Isozaki, Hiroaki Yasuzaki, Hajime Takase, Yuichiro Yano, Kouichi Tamura","doi":"10.1186/s12911-025-03034-3","DOIUrl":"10.1186/s12911-025-03034-3","url":null,"abstract":"<p><strong>Background: </strong>Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatigue by integrating metabolomic, microbiome, and genetic data from blood and saliva samples using a multi-omics approach.</p><p><strong>Methods: </strong>Metabolomic, microbiome, and single nucleotide polymorphisim analyses were conducted on saliva and blood samples from 52 patients with NCDs. Fatigue dimensions were assessed using the Multidimensional Fatigue Inventory and correlated with biological markers. LightGBM, a gradient boosting algorithm, was used for fatigue prediction, and model performance was evaluated using the F1-score, accuracy, and receiver operating characteristic area under the curve using leave-one-out cross-validation. Statistical analyses included correlation tests and multiple comparison adjustments (p < 0.05; false discovery rate <0.05). This study was approved by the Yokohama City University Hospital Ethics Committee (F230100022).</p><p><strong>Results: </strong>Plasmalogen synthesis was significantly associated with physical fatigue in both blood and saliva samples. Additionally, homocysteine degradation and catecholamine biosynthesis in the blood were significantly associated with mental fatigue (Holm p < 0.05). Microbial imbalances, including reduced levels of Firmicutes negativicutes and Patescibacteria saccharimonadia, correlated with general and physical fatigue (r = - 0.379, p = 0.006). Genetic variants in genes, such as GPR180, NOTCH3, SVIL, HSD17B11, and PLXNA1, were linked to various fatigue dimensions (r range: -0.539-0.517, p < 0.05). Machine learning models based on blood and salivary biomarkers achieved an F1-score of approximately 0.7 in predicting fatigue dimensions.</p><p><strong>Conclusion: </strong>This study provides preliminary insights into the potential involvement of alterations in lipid metabolism, catecholamine biosynthesis disruptions, microbial imbalances, and specific genetic variants in fatigue in patients with NCDs. These findings lay the groundwork for personalized interventions, although further validation and model refinement across diverse populations are needed to enhance the prediction performance and clinical applicability.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"204"},"PeriodicalIF":3.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General practitioners' perceptions on decision aids in healthcare: a qualitative study in Portugal.","authors":"Mafalda Proença-Portugal, Bruno Heleno, Sónia Dias, Ana Gama, Sofia Baptista","doi":"10.1186/s12911-025-03044-1","DOIUrl":"10.1186/s12911-025-03044-1","url":null,"abstract":"<p><strong>Background: </strong>Decision aids (DA) are evidence-based tools that support health-related decisions. Despite their recognised value, the use of DAs in primary care remains modest. In Portugal, clinical guidelines focus on clinical decision-making with minimal patient engagement. Adapting international DAs to the Portuguese context could be an efficient way to support the transition to shared decision-making. Understanding general practitioners' (GPs) awareness and perceptions of DAs is essential before evaluating their willingness to adopt these tools for specific clinical problems.</p><p><strong>Aim: </strong>To explore Portuguese GPs' perceptions of DAs and their implementation in primary care.</p><p><strong>Method: </strong>Qualitative study with GPs and GP trainees in Portugal. Seven online focus groups were conducted with 33 GPs and GP trainees selected through purposive sampling. Data were analysed using deductive content analysis.</p><p><strong>Results: </strong>Most participants initially confused DAs with clinical decision support tools; only one recognised them as aids for shared decision-making. After clarification, GPs expressed favourable attitudes and believed that patients were willing to use DAs. Key barriers to adoption included limited funding, time constraints, and the lack of Portuguese translations. Facilitators involved system integration and localisation. Priority topics centred on prevention (screening, statin use, vaccines, contraception, lifestyle changes) and specific medications (antibiotics, hormone replacement, psychotropics).</p><p><strong>Conclusion: </strong>Although unfamiliar to most participants, integrating DAs in primary care was well received, and these tools may provide added value in improving the quality of health decisions.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"202"},"PeriodicalIF":3.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12131423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using self-generated identification codes to match anonymous longitudinal data in a sexual health study of secondary school students: a cohort study.","authors":"Edmond Pui Hang Choi, Ellie Bostwick Andres, Heidi Sze Lok Fan, Lai Ming Ho, Alice Wai Chi Fung, Kevin Wing Chung Lau, Neda Hei Tung Ng, Monique Yeung, Janice Mary Johnston","doi":"10.1186/s12911-025-03028-1","DOIUrl":"10.1186/s12911-025-03028-1","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to (i) describe the procedures for generating self-generated identification codes (SGICs) in a prospective longitudinal evaluation of a sexual health program for secondary school students in Hong Kong; (ii) outline the matching strategies and processes; (iii) examine rates of successful matching and associated factors; and (iv) compare the responses of participants whose data could be matched to those whose data could not.</p><p><strong>Methods: </strong>A prospective longitudinal cohort study was conducted. The SGIC comprised a 5-element code with 4 digits and 3 letters. A matching algorithm was developed to link baseline and follow-up data collected from students in Years 1 to 3 (n = 1,064) during the 2019-2020 school year. Matching success and associated factors were analyzed, and responses from matched and unmatched participants were compared.</p><p><strong>Results: </strong>The rate of perfectly matched cases was 49.06%, while 23.59% were partially matched, and 27.35% were unmatched. Logistic regression analysis revealed that male students (adjusted odds ratio [aOR]: 0.63) and Year 1 students (vs. Year 3; aOR: 0.56) were less likely to be perfectly matched. Compared to unmatched cases, perfectly and partially matched cases were less likely to have missing values and more likely to exhibit positive attitudes toward the sexual health program and related topics, such as the importance of sexual health, equal relationships, and condom use.</p><p><strong>Conclusion: </strong>The use of SGICs successfully matched approximately 72.65% of the study sample over a one-year period. These findings highlight the potential of SGICs as a tool for longitudinal data matching while underscoring the need for further refinement of code generation processes and matching algorithms to minimize data wastage and improve effectiveness.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"201"},"PeriodicalIF":3.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12131654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}