BMC Medical Informatics and Decision Making最新文献

{"title":"Effectiveness of various general large language models in clinical consensus and case analysis in dental implantology: a comparative study.","authors":"Yuepeng Wu, Yukang Zhang, Mei Xu, Chen Jinzhi, Yican Xue, Yuchen Zheng","doi":"10.1186/s12911-025-02972-2","DOIUrl":"10.1186/s12911-025-02972-2","url":null,"abstract":"<p><strong>Background: </strong>This study evaluates and compares ChatGPT-4.0, Gemini Pro 1.5(0801), Claude 3 Opus, and Qwen 2.0 72B in answering dental implant questions. The aim is to help doctors in underserved areas choose the best LLMs(Large Language Model) for their procedures, improving dental care accessibility and clinical decision-making.</p><p><strong>Methods: </strong>Two dental implant specialists with over twenty years of clinical experience evaluated the models. Questions were categorized into simple true/false, complex short-answer, and real-life case analyses. Performance was measured using precision, recall, and Bayesian inference-based evaluation metrics.</p><p><strong>Results: </strong>ChatGPT-4 exhibited the most stable and consistent performance on both simple and complex questions. Gemini Pro 1.5(0801)performed well on simple questions but was less stable on complex tasks. Qwen 2.0 72B provided high-quality answers for specific cases but showed variability. Claude 3 opus had the lowest performance across various metrics. Statistical analysis indicated significant differences between models in diagnostic performance but not in treatment planning.</p><p><strong>Conclusions: </strong>ChatGPT-4 is the most reliable model for handling medical questions, followed by Gemini Pro 1.5(0801). Qwen 2.0 72B shows potential but lacks consistency, and Claude 3 Opus performs poorly overall. Combining multiple models is recommended for comprehensive medical decision-making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"147"},"PeriodicalIF":3.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718074","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":"Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.","authors":"Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh","doi":"10.1186/s12911-025-02982-0","DOIUrl":"10.1186/s12911-025-02982-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or cardiovascular complications, while delays can strain hospital capacity. Machine learning algorithms offer a promising solution by leveraging large amounts of patient data to predict optimal discharge times. Unlike prior studies relying on statistical models or single-algorithm methods, this research assesses multiple ML models to predict discharge readiness, comparing them against staff evaluations and the Aldrete checklist.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methodology: &lt;/strong&gt;We conducted a cross-sectional study of 830 patients under general anesthesia from December 2023 to April 2024, collecting demographics, surgical details, and Aldrete scores. A power analysis ensured statistical robustness, targeting a 5% accuracy improvement (minimum clinically important difference, derived from Gabriel et al., 2017), with variance (SD ≈ 0.1) from pilot data, using a two-sample t-test (power = 0.8, alpha = 0.05), confirming the sample size's adequacy. Two prediction approaches were tested: discharge timing in 15-minute intervals and binary classification (within 15 min or later). Models included Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and XGBoost, assessed via accuracy, precision, recall, F1 score, and AUC. Predictions were benchmarked against staff and Aldrete scores, with 99.5% confidence intervals (CIs) adjusting for multiple comparisons.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;he RF algorithm showed high performance in both prediction approaches. In the first approach, RF achieved an AUC of 0.75 (99.5% CI: 0.70-0.80) and accuracy of 0.87 (99.5% CI: 0.83-0.91) per staff evaluations, and an AUC of 0.87 (99.5% CI: 0.83-0.91) and accuracy of 0.71 (99.5% CI: 0.66-0.76) per Aldrete scores. In the second approach, RF recorded an AUC of 0.85 (99.5% CI: 0.81-0.89) and accuracy of 0.86 (99.5% CI: 0.82-0.90) per staff evaluations, with ANN also showing strong results (AUC = 0.88, 99.5% CI: 0.84-0.92; accuracy = 0.78, 99.5% CI: 0.74-0.82). Due to overlapping CIs, differences between models were not statistically significant (P &gt;.005). According to the Aldrete checklist, RF, SVM, and ANN exhibited competitive predictive capability, with AUCs ranging from 0.80 to 0.86.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The strong performance of Random Forest (RF) and Artificial Neural Network (ANN) models in predicting PACU discharge timing upon admission highlights their potential as effective tools for evaluating discharge readiness, as compared to staff assessments and the Aldrete checklist. This study focused on assessing these models, showing their ability to produce consistent predictions, though differences between to","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"146"},"PeriodicalIF":3.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708628","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":"COVID-19 vaccination and test management for healthcare workers-development, implementation and feasibility of a custom human resources information platform at a university hospital.","authors":"Matthias Bonigut, Ana Zhelyazkova, Mathias Weber, Stefanie Geiser-Metz, Markus Geis, Bernhard Heindl, Stephan Prückner","doi":"10.1186/s12911-025-02974-0","DOIUrl":"10.1186/s12911-025-02974-0","url":null,"abstract":"<p><strong>Background: </strong>The continuously evolving legislative and reporting requirements during the COVID-19 pandemic posed the demand for establishing an efficient real-time human resources management system at the LMU University Hospital, one of the largest university hospitals in Germany. Developing a system allowing for agile real-time analysis as well as for reporting employees' COVID-19 vaccination and testing status while ensuring the security of personnel data presented several technical and managerial challenges.</p><p><strong>Methods: </strong>We designed and implemented a custom COVID-19 human resources information platform in order to fulfill the LMU University Hospital's legal requirement to report employees' vaccination and testing status. We designed the platform as an all-in-one solution for all relevant COVID-19 data, merged from five individual sources. The development process was guided by the principles of findability, accessibility, interoperability and reusability (FAIR) with particular focus on interoperability. Here, we present the platform's design, cumulative user data and discuss the feasibility of the approach including its intended and unintended outcomes.</p><p><strong>Results: </strong>The COVID-19 human resources management platform was the first solution of its kind at the LMU University Hospital, emerging from the specific need for an efficient exterior and interior mandate fulfillment. It served both for operational management purposes as well as for strategic pandemic and hospital management. The immediate dependency on data privacy and regulatory adaptations due to the evolving pandemic situation posed the necessity for regular adaptations to the platform's structure.</p><p><strong>Conclusions: </strong>The presented case reveals how data utilization requires the concurrent and proactive consideration of data security and interoperability against the background of a scalable architecture. Simultaneously, the development of such platforms needs to be open to new cases, functions and sources, thus requiring a dynamic and agile environment.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"142"},"PeriodicalIF":3.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699688","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":"Understanding EMS response times: a machine learning-based analysis.","authors":"Peter Hill, Jakob Lederman, Daniel Jonsson, Peter Bolin, Veronica Vicente","doi":"10.1186/s12911-025-02975-z","DOIUrl":"10.1186/s12911-025-02975-z","url":null,"abstract":"<p><strong>Background: </strong>Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning (ML) techniques to identify key factors such as urgency levels, environmental conditions, and geographic variables. The findings aim to inform strategies for enhancing resource allocation and operational efficiency in EMS systems.</p><p><strong>Methods: </strong>A retrospective analysis was conducted using over one million EMS missions from Stockholm, Sweden, between 2017 and 2022. Advanced ML techniques, including Gradient Boosting models, were applied to evaluate the influence of diverse variables such as call handling times, travel times, weather patterns, and resource availability. Feature engineering was employed to extract meaningful insights, and statistical models were used to validate the relationships between key predictors and response times.</p><p><strong>Results: </strong>The study revealed a complex interplay of factors influencing EMS response times, aligning with the study's aim to deepen the understanding of these determinants. Key drivers of response time variability included weather conditions, call priority, and resource constraints. ML models, particularly Gradient Boosting, proved effective in quantifying these impacts and provided robust predictions of response times across scenarios. By providing a comprehensive evaluation of these influences, the results support the development of adaptive resource allocation models and evidence-based policies aimed at enhancing EMS efficiency and equity across all call priorities.</p><p><strong>Conclusions: </strong>This study underscores the potential of ML-driven insights to revolutionize EMS resource allocation strategies. By integrating real-time data on weather, call types, and workload, EMS systems can transition to adaptive deployment models, reducing response times and enhancing equity across priority levels. The research provides a blueprint for implementing predictive analytics in EMS operations, paving the way for evidence-based policies that improve emergency care efficiency and outcomes.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"143"},"PeriodicalIF":3.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699765","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":"Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video).","authors":"Jian Chen, Ganhong Wang, Kaijian Xia, Zhenni Wang, Luojie Liu, Xiaodan Xu","doi":"10.1186/s12911-025-02973-1","DOIUrl":"10.1186/s12911-025-02973-1","url":null,"abstract":"<p><strong>Objective: </strong>In the functional assessment of the esophagogastric junction (EGJ), the endoscopic Hill classification plays a pivotal role in classifying the morphology of the gastroesophageal flap valve (GEFV). This study aims to develop an artificial intelligence model for Hill classification to assist endoscopists in diagnosis, covering the entire process from model development, testing, interpretability analysis, to multi-terminal deployment.</p><p><strong>Method: </strong>The study collected four datasets, comprising a total of 1143 GEFV images and 17 gastroscopic videos, covering Hill grades I, II, III, and IV. The images were preprocessed and enhanced, followed by transfer learning using a pretrained model based on CNN and Transformer architectures. The model training utilized a cross-entropy loss function, combined with the Adam optimizer, and implemented a learning rate scheduling strategy. When assessing model performance, metrics such as accuracy, precision, recall, and F1 score were considered, and the diagnostic accuracy of the AI model was compared with that of endoscopists using McNemar's test, with a p-value < 0.05 indicating statistical significance. To enhance model transparency, various interpretability analysis techniques were used, including t-SNE, Grad-CAM, and SHAP. Finally, the model was converted into ONNX format and deployed on multiple device terminals.</p><p><strong>Results: </strong>Compared through performance metrics, the EfficientNet-Hill model surpassed other CNN and Transformer models, achieving an accuracy of 83.32% on the external test set, slightly lower than senior endoscopists (86.51%) but higher than junior endoscopists (75.82%). McNemar's test showed a significant difference in classification performance between the model and junior endoscopists (p < 0.05), but no significant difference between the model and senior endoscopists (p ≥ 0.05). Additionally, the model reached precision, recall, and F1 scores of 84.81%, 83.32%, and 83.95%, respectively. Despite its overall excellent performance, there were still misclassifications. Through interpretability analysis, key areas of model decision-making and reasons for misclassification were identified. Finally, the model achieved real-time automatic Hill classification at over 50fps on multiple platforms.</p><p><strong>Conclusion: </strong>By employing deep learning to construct the EfficientNet-Hill AI model, automated Hill classification of GEFV morphology was achieved, aiding endoscopists in improving diagnostic efficiency and accuracy in endoscopic grading, and facilitating the integration of Hill classification into routine endoscopic reports and GERD assessments.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"144"},"PeriodicalIF":3.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699677","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":"Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.","authors":"Kajornvit Raghareutai, Watcharaporn Tanchotsrinon, Onuma Sattayalertyanyong, Uayporn Kaosombatwattana","doi":"10.1186/s12911-025-02969-x","DOIUrl":"10.1186/s12911-025-02969-x","url":null,"abstract":"<p><strong>Background: </strong>Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, endoscopic intervention is the mainstay treatment for hemostasis in high-risk rebleeding lesions. Predicting the need for endoscopic intervention would be beneficial in resource-limited areas for selective referral to an endoscopic center. The proposed risk stratification scores had limited accuracy. We developed a machine learning model to predict the need for endoscopic intervention in patients with acute UGIB.</p><p><strong>Methods: </strong>A prospectively collected database of UGIB patients from 2011 to 2020 was retrospectively reviewed. Patients older than 18 years diagnosed with UGIB who underwent endoscopy were included. Data comprised demographic characteristics, clinical presentation, and laboratory parameters. The cleaned data was used for model development and validation in Python. We conducted 80%-20% split sample training and test sets. The training set was used for supervised learning of 15 models using a stratified 5-fold cross-validation process. The model with the highest AUROC was then internally validated with the test set to evaluate performance.</p><p><strong>Results: </strong>Of 1389 patients, 615 (44.3%) of the cohorts received the endoscopic intervention (293 variceal- and 336 nonvariceal-bleeding interventions). Eighteen features, including demographic characteristics, clinical presentation, and laboratory parameters, were selected as input for 15 machine learning models. The result revealed that the linear discriminant analysis model could achieve the highest AUROC of 0.74 to predict endoscopic intervention. The model was validated with the test set, in which the AUROC was increased from 0.74 to 0.81. Finally, the model was deployed as a web application by Streamlit.</p><p><strong>Conclusions: </strong>Our machine learning model can identify patients with acute UGIB who need endoscopic intervention with good performance. This may help primary care physicians prioritize patients who need referrals and optimize resource allocation in resource-limited areas. Further development and identification of more specific features might improve prediction performance.</p><p><strong>Trial registration: </strong>None (Retrospective cohort study) PATIENT & PUBLIC INVOLVEMENT: None.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"145"},"PeriodicalIF":3.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699690","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":"Survival analysis using machine learning in transplantation: a practical introduction.","authors":"Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque","doi":"10.1186/s12911-025-02951-7","DOIUrl":"10.1186/s12911-025-02951-7","url":null,"abstract":"<p><strong>Background: </strong>Survival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms.</p><p><strong>Methods: </strong>We employed a RSF model to analyze a simulated dataset of kidney transplant recipients. The data were split into training, validation, and test sets using split sample (70%-30%) and cross-validation (5-folds) techniques to evaluate model performance. Hyperparameter tuning strategies were employed to select the best model. The concordance index (C-index) and Integrated Brier Score (IBS) were used for internal validation. Additionally, time-dependent AUC, F1 score, accuracy, and precision were evaluated to provide a comprehensive assessment of the model's predictive performance. Finally, a Cox Proportional Hazards model was fitted to compare the results of the main metrics between both models. All analyses were supported by step-by-step code to ensure reproducibility.</p><p><strong>Findings: </strong>The RSF model obtained a C-index of 0.774, an IBS of 0.090. The F1 score was of 0.945, accuracy was 89.67 and precision was 90.99%. The time-dependent ROC analysis produced an AUC of 0.709, indicating a moderate predictive performance. Lastly, the analysis shows that the three most important variables are donor age, BMI, and recipient age.</p><p><strong>Conclusions: </strong>This study demonstrates the robustness and potential of the RSF model in kidney transplant analysis, achieving strong validation metrics and highlighting its advantages in managing complex, censored data, while emphasizing the need for further exploration of hybrid models and clinical integration.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"141"},"PeriodicalIF":3.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676851","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 novel method for subgroup discovery in precision medicine based on topological data analysis.","authors":"Ciara F Loughrey, Sarah Maguire, Paweł Dłotko, Lu Bai, Nick Orr, Anna Jurek-Loughrey","doi":"10.1186/s12911-025-02852-9","DOIUrl":"10.1186/s12911-025-02852-9","url":null,"abstract":"<p><strong>Background: </strong>The Mapper algorithm is a data mining topological tool that can help us to obtain higher level understanding of disease by visualising the structure of patient data as a similarity graph. It has been successfully applied for exploratory analysis of cancer data in the past, delivering several significant subgroup discoveries. Using the Mapper algorithm in practice requires setting up multiple parameters. The graph then needs to be manually analysed according to a research question at hand. It has been highlighted in the literature that Mapper's parameters have significant impact on the output graph shape and there is no established way to select their optimal values. Hence while using the Mapper algorithm, different parameter values and consequently different output graphs need to be studied. This prevents routine application of the Mapper algorithm in real world settings.</p><p><strong>Methods: </strong>We propose a new algorithm for subgroup discovery within the Mapper graph. We refer to the task as hotspot detection as it is designed to identify homogenous and geometrically compact subsets of patients, which are distinct with respect to their clinical or molecular profiles (e.g. survival). Furthermore, we propose to include the existence of a hotspot as a criterion while searching the parameter space, addressing one of the key limitations of the Mapper algorithm (i.e. parameter selection).</p><p><strong>Results: </strong>Two experiments were performed to demonstrate the efficacy of the algorithm, including an artificial hotspot in the Two Circles dataset and a real world case study of subgroup discovery in oestrogen receptor-positive breast cancer. Our hotspot detection algorithm successfully identified graphs containing homogenous communities of nodes within the Two Circles dataset. When applied to gene expression data of ER+ breast cancer patients, appropriate parameters were identified to generate a Mapper graph revealing a hotspot of ER+ patients with poor prognosis and characteristic patterns of gene expression. This was subsequently confirmed in an independent breast cancer dataset.</p><p><strong>Conclusions: </strong>Our proposed method can be effectively applied for subgroup discovery with pathology data. It allows us to find optimal parameters of the Mapper algorithm, bridging the gap between its potential and the translational research.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"139"},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656225","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":"Evaluation and validation of a clinical decision support system for dose optimisation in hospitalized patients with (morbid) obesity - a retrospective, observational study.","authors":"Lianne Brand, L Mitrov-Winkelmolen, T M Kuijper, T M Bosch, L L Krens","doi":"10.1186/s12911-025-02963-3","DOIUrl":"10.1186/s12911-025-02963-3","url":null,"abstract":"<p><strong>Background: </strong>Obesity changes a patient's pharmacokinetics and pharmacotherapeutic advices should be personalized to ensure proper treatment. Currently, implementations of advices regarding the obese population are lacking and weight and body mass index (BMI) are rarely monitored. The Maasstad Hospital built a clinical decision support system (CDSS) for pharmacists, based on current Dutch guidelines, to supply therapeutic advices for (morbidly) obese patients based on patient characteristics. In this study we evaluated whether patients receiving inadequate pharmacotherapy are indeed identified via this CDSS and to which extent irrelevant alerts are generated. Moreover, it is investigated to which extent pharmacists carry on the generated advices and to which extent physicians act upon these.</p><p><strong>Methods: </strong>The research concerned a retrospective observational study performed at the Maasstad Hospital in Rotterdam, the Netherlands between January 2021 and august 2021. The drugs included were dalteparin, apixaban, dabigatran, edoxaban, rivaroxaban, vancomycin and ciprofloxacin. Dispensing data, patient characteristics and CDSS processing were collected. Dispensing data was included when the patient's weight or BMI could potentially lead to dose adjustments via the CDSS. The CDSS was evaluated for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Additionally, excess alerts, defined as irrelevant alerts on the moment of assessment, of the CDSS and adherence to the CDSS were investigated.</p><p><strong>Results: </strong>1218 alerts over 3735 drug dispenses were generated. 568 alerts (46.6%) resulted in a pharmacotherapeutic advice by the pharmacist to the physician. In most cases, the sensitivity, specificity, PPV and NPV were 100.0% with varying 95% CIs. For some drugs technical adjustments were needed, including the initially incorrect BMI setting of vancomycin within the CDSS, resulting in a high excess alerts of 56.9%. Dabigatran had a NPV of 22.2% 95% CI [6.3-54.7] and a sensitivity of 56.3% 95% CI [33.2-76.9]. Overall excess alerts varied from 22.2% to 56.9%. Depending on the drug, the advices resulted in 6.9-100.0% real pharmacotherapy adjustments in practise.</p><p><strong>Conclusion: </strong>The (morbid) obesity CDSS functions as expected and identifies the (morbidly) obese patients with inadequate pharmacotherapy. The adherence of physicians and the follow-up in practise varies widely and requires further investigation.</p><p><strong>Trial registration: </strong>Non-WMO research W21.218.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"140"},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662322","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 study on large-scale disease causality discovery from biomedical literature.","authors":"Shirui Yu, Peng Dong, Junlian Li, Xiaoli Tang, Xiaoying Li","doi":"10.1186/s12911-025-02893-0","DOIUrl":"10.1186/s12911-025-02893-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Biomedical semantic relationship extraction could reveal important biomedical entities and the semantic relationships between them, providing a crucial foundation for the biomedical knowledge discovery, clinical decision making and other artificial intelligence applications. Identifying the causal relationships between diseases is a significant research field, since it expedites the identification of underlying disease pathogenesis mechanisms and promote better disease prevention and treatment. SemRep is an effective tool for semantic relationship extraction in the biomedical field, but it is not accurate enough for disease causality extraction, bringing challenges for downstream tasks. In this study, we proposed an optimization strategy for SemRep to enhance its accuracy in disease causality extraction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study aims to optimize disease causality extraction of SemRep tool by constructing a semantic predicate vocabulary that precisely expresses disease causality to support the automatic extraction of disease causality knowledge from biomedical literature. The proposed method invloves the following four steps: Firstly, we obtained a collection of semantic feature words expressing disease causality based on current causality predicate studies and the disease causality pairs extracted from SemMedDB. Then, we constructed a disease causality semantic predicate vocabulary by filtering and evaluating the clue words using quantitative comparisons. Following that, we extracted disease causality pairs from the biomedical literature using 36 semantic predicates with an accuracy greater than 80% for more meaningful knowledge discovery. Finally, we conducted knowledge discovery based on the extracted disease causality triples, which primarily includes unidirectional disease causality, bidirectional disease causality, as well as two specific types of disease causality: primary disease causality and rare disease causality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We obtained a disease causality semantic predicate vocabulary containing 50 textual predicates with an accuracy of above 40%. 36 semantic predicates from the 60% accuracy group were used for disease causality extraction, yielding 259,434 disease causality pairs for subsequent knowledge discovery. Among them, 92,557 types with 176,010 unidirectional disease causality triples, and 6084 types with 83,424 bidirectional disease causality triples were found eventually. Two other types of disease causality, primary disease causality and rare disease causality, were also discovered.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The novelty of this research is that the proposed method enhanced the disease causality extraction of SemRep tool, resulting a more accurate and comprehensive disease causality extraction. It also facilitates an automatic disease causality extraction from large-scale biomedical literature. Additionally, a customized extraction of disease ca","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"136"},"PeriodicalIF":3.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11916938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656227","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}