BMC Medical Informatics and Decision Making最新文献

{"title":"Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models.","authors":"Feifei Shen, Ying Xu, Xusheng Jiang, Linjie Yu, Hong-Han Ge, Xu Wang, Yan-Qun Sun","doi":"10.1186/s12911-025-03211-4","DOIUrl":"10.1186/s12911-025-03211-4","url":null,"abstract":"<p><strong>Objective: </strong>To develop and evaluate machine learning models combined with survival analysis for predicting 7-, 14-, and 28-day mortality in critically ill children with acute kidney injury (AKI), identifying key predictors to guide risk stratification and early intervention.</p><p><strong>Methods: </strong>Using the Pediatric Intensive Care (PIC) database, we analyzed data from 3,624 children with AKI admitted between 2010 and 2018. Nine machine learning algorithms, including CatBoost, were trained to predict mortality, with feature importance assessed via SHapley Additive exPlanations (SHAP). Time-to-event analyses, including Kaplan-Meier and restricted cubic spline methods, examined the temporal impact of predictors on 28-day mortality, stratified by age and AKI stage.</p><p><strong>Results: </strong>CatBoost achieved the highest area under the curve (AUC) values: 0.871 (95% CI: 0.824-0.918) for 7-day, 0.871 (95% CI: 0.829-0.913) for 14-day, and 0.867 (95% CI: 0.829-0.905) for 28-day mortality. Lactate was the top predictor across all models. Time-to-event analyses revealed a linear association between elevated lactate (cut-off: 1.5 mmol/L) and 28-day mortality (p-overall < 0.001), with stronger effects in infants (0-3 years) and AKI stage 1 patients (HR > 1).</p><p><strong>Conclusions: </strong>Machine learning, particularly CatBoost, combined with survival analysis, accurately predicts AKI-related mortality in critically ill children, with lactate as a pivotal marker. These findings support precision risk stratification and early lactate-targeted interventions, though multicenter validation is needed for clinical adoption.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"371"},"PeriodicalIF":3.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145273940","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":"Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach.","authors":"Nele Albers, Mark A Neerincx, Willem-Paul Brinkman","doi":"10.1186/s12911-025-03164-8","DOIUrl":"10.1186/s12911-025-03164-8","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"370"},"PeriodicalIF":3.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145273917","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":"Factors influencing the availability and use of electronic medical records systems in public health facilities in Uganda: a cross-sectional assessment.","authors":"Anthony Ddamba, Benard Nsubuga, Moses Kamabare, Ernest Abaho, Kassim Alinda, David Arinaitwe, Phillip Ampaire, Harriet Akello","doi":"10.1186/s12911-025-03190-6","DOIUrl":"10.1186/s12911-025-03190-6","url":null,"abstract":"<p><strong>Background: </strong>The advancement of information and communication technology (ICT) has significantly accelerated the adoption and utilisation of Electronic Medical Record (EMR) systems in both developed and developing countries. This study aimed to examine a comprehensive understanding of the current EMR systems landscape in Uganda, examining the integration of ICT into healthcare delivery and the associated factors.</p><p><strong>Methods: </strong>A cross-sectional study design was utilised to conduct this study across 265 government-owned health facilities. Quantitative data were collected through semi-structured questionnaires, while qualitative insights were obtained via face-to-face interviews. Associations between categorical variables were assessed using linear-by-linear association and Cochrane-Armitage chi-square tests. Analysis of Variance was used for comparison of group mean differences while Tukey's HSD and Scheffe's test were used for post-hoc comparisons. Poisson regression analysis was applied to determine the factors influencing the availability and utilisation of EMR systems in health facilities.</p><p><strong>Results: </strong>On average, each health facility utilised 4.81 EMR systems (SD = 1.41). Statistically significant differences in the average number of EMR systems were observed across different levels of care (P-value = 0.0108). However, interoperability between EMR systems was reported in only 10% (26/265) of facilities. Regional referral hospitals were 36% more likely to have a higher number of EMR systems compared to health centre IVs (P-value = 0.0001), while general hospitals were 12% more likely (P-value = 0.041). Facilities employing a hybrid medical records system were 1.2 times more likely to utilise EMR systems compared to those using exclusively electronic systems (P-value = 0.027). Additionally, facilities with a higher number of medical departments demonstrated a significantly positive association with EMR system usage (P-value = 0.020).</p><p><strong>Conclusions: </strong>The study identified a diverse array of EMR systems across public health facilities, many of which exhibit technical disparities and are designed to serve narrowly defined institutional interests. Critically, the absence of a unified national framework or standard guidelines governing EMR adoption and interoperability continues to hinder the seamless exchange of clinical and patient data. To advance digital health integration, it is imperative to expedite the implementation of national interoperability frameworks alongside the operationalisation of a centralised health data warehouse.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"372"},"PeriodicalIF":3.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145273819","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":"Barriers and facilitators to shared decision-making for patients with cancer and health care providers based on the COM-B model: a systematic review.","authors":"Lisi Duan, Ting Wang, Yinning Guo, Zhongmin Fu, Ting Xu, Ping Zhu, Liuliu Zhang, Shijuan Gao, Qin Xu, Chulei Tang","doi":"10.1186/s12911-025-03194-2","DOIUrl":"10.1186/s12911-025-03194-2","url":null,"abstract":"<p><strong>Background: </strong>With advancements in cancer treatment approaches, patients face increasingly complex decisions regarding their care and treatment. Although Shared Decision-Making (SDM) can help patients make more informed and optimal choices, its development remains limited, and it has not been widely integrated into clinical practice. Identifying the barriers and facilitators to SDM from the perspective of patients and health care providers (HCPs) is essential. The Capability, Opportunity, and Motivation Model of Behaviour (COM-B) provides a framework for understanding these factors.</p><p><strong>Objective: </strong>This review aimed to explore the barriers and facilitators of SDM between patients and HCPs on the basis of the COM-B and to identify key common and dual-effect factors.</p><p><strong>Methods: </strong>Seven databases were searched for qualitative, quantitative, and mixed-methods studies. Data on the study design and key findings were extracted and analyzed guided by the COM-B model. The findings were reported in accordance with the PRISMA guidelines. Study quality was appraised via the Mixed Methods Appraisal Tool.</p><p><strong>Results: </strong>A total of 6,811 papers were identified, 32 of which met the inclusion criteria. From these studies, 64 key barriers and facilitators influencing the implementation of SDM from the perspective of patients and HCPs were extracted. These factors were systematically categorized according to the subcomponents of the COM-B model: physical capability (e.g, poor health status), psychological capability (e.g, inaccurate understanding of the disease), reflective motivation (e.g, conflicting goals), automatic motivation (e.g, fear of cancer), physical opportunity (e.g, supplemental resources), and social opportunity (e.g, good family support).</p><p><strong>Conclusion: </strong>Guided by the COM-B model, this study identified factors associated with SDM among patients with cancer and HCPs. Further analysis revealed that some factors are shared by both groups; interventions targeting these common factors may produce simultaneous effects on patients and HCPs, offering more implementation value. Other factors exhibit dual characteristics, acting as both facilitators and barriers depending on the context. Future efforts should focus on exploring mechanisms to address such barriers into facilitators in specific clinical settings, thereby promoting the widespread implementation of SDM.</p><p><strong>Registration number: </strong>Our protocol was registered in PROSPERO (ID: CRD42024568101).</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"369"},"PeriodicalIF":3.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145273834","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":"Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data.","authors":"Niloofar Shabani, Mehdi Yaseri, Rasoul Alimi, Fatemeh Nazemian, Hojjat Zeraati","doi":"10.1186/s12911-025-03205-2","DOIUrl":"10.1186/s12911-025-03205-2","url":null,"abstract":"<p><strong>Background: </strong>In some survival studies, longitudinal biomarkers, along with baseline covariates, play crucial roles in predicting patient survival. Dynamic prediction models that incorporate updated longitudinal marker information offer updated survival predictions for patients. In this study, we employ a combination of the nonparametric gradient boosting machine learning algorithm and the landmark approach, which not only facilitates dynamic prediction but also circumvents the limitations of classical methods.</p><p><strong>Methods: </strong>We conducted two simulation studies under different scenarios to compare three dynamic prediction models: the joint model, the Cox landmarking model, and the Landmarking Gradient Boosting Model (LGBM). We compared the three dynamic survival prediction methods using AUC (Area Under the Curve) and Brier score metrics. Using the LGBM, we performed dynamic prediction at various landmark times on a real kidney transplant dataset in the presence of two longitudinal markers.</p><p><strong>Results: </strong>Simulation studies demonstrated that when there was a simple linear relationship between longitudinal markers and the survival process, the joint model outperformed both Cox landmarking and LGBM in terms of higher AUC (better discrimination) and lower Brier score (better overall performance) indices. Conversely, in scenarios characterized by complex and nonlinear relationships between longitudinal markers and the survival process, the LGBM outperformed the two classical methods, under conditions involving larger sample sizes (n = 1000, 1500 vs. n = 300, 650), higher censoring rates (90% vs. 30%, 50%), and later landmark times (3.5, 5, 6.5 vs. 0.5, 2). The application of LGBM to real kidney transplant data revealed that at early landmark time points, factors such as blood urea nitrogen (BUN) (variable importance [VIMP] = 0.34), age (VIMP = 0.26), creatinine (VIMP = 0.24), hypertension (VIMP = 0.10), and gender (VIMP = 0.06) were associated with the risk of kidney transplant failure. At subsequent landmark time points, creatinine, BUN, and age emerged as the most important factors associated with kidney allograft failure.</p><p><strong>Conclusions: </strong>Our findings demonstrate that in situations where the relationships between variables are complex and the proportional hazards assumption does not hold, the LGBM method performs better than Cox landmarking and joint modeling for dynamic survival prediction in cases with large sample sizes, high censoring rates, and later landmark times.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"368"},"PeriodicalIF":3.8,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12513103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145257582","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":"Beyond NT-proBNP and troponin: How machine learning redefines light-chain cardiac amyloidosis risk assessment.","authors":"Danni Wu, Xiaohang Liu, Xinhao Li, Jia Chen, Xue Lin, Ligang Fang, Wei Chen","doi":"10.1186/s12911-025-03207-0","DOIUrl":"10.1186/s12911-025-03207-0","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"367"},"PeriodicalIF":3.8,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145257484","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 of a big data platform for collecting and utilizing clinical information from the Korea Biobank Network.","authors":"Yun Seon Im, Seol Whan Oh, Ki Hoon Kim, Wona Choi, In Young Choi","doi":"10.1186/s12911-025-03192-4","DOIUrl":"10.1186/s12911-025-03192-4","url":null,"abstract":"<p><strong>Background: </strong>Advanced biobanks increasingly focus on supporting biomedical research through the collection and integration of large-scale biological and clinical datasets. This study aimed to develop a big data platform that enables institutions within the Korea Biobank Network (KBN) to efficiently collect and utilize clinical information using a standardized common data model.</p><p><strong>Methods: </strong>The KBN Biobank Research Information and Digital Image Exchange (BRIDGE) platform was developed to allow 43 biobanks to systemically collect and upload electronic medical records and clinical data. This platform was designed to incorporate automated quality verification and basic statistical preprocessing functionalities, allowing users to analyze data efficiently without complex queries. Additionally, a survey was conducted to evaluate user satisfaction with the platform.</p><p><strong>Results: </strong>Through the KBN BRIDGE platform, institutions collected and integrated clinical information on 39 diseases. A total of 136,473 patients' clinical data, collected by institutions between 2021 and 2023, were uploaded to the KBN common data model, including 43,330 serum samples, 33,352 plasma samples, and 22,279 buffy coat samples. A satisfaction survey conducted among 35 institutional data managers reported an average score of 3.5 out of 5 for the platform.</p><p><strong>Conclusions: </strong>This study developed and demonstrated that the KBN BRIDGE platform enables institutions to systematically collect, integrate, and manage large-scale clinical information across multiple biobanks. Furthermore, through data quality management and preprocessing statistical functions, the platform has shown potential for several research applications. Future improvements in system functionality and clinical information utilization can further enhance the platform's utility across various research fields.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"366"},"PeriodicalIF":3.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12505599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145249946","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 external validation of a machine learning-based predictive model for acute kidney injury in hospitalized children with idiopathic nephrotic syndrome.","authors":"Xuejun Yang, De Zhang, Yan Li, Anshuo Wang, Zongwen Chen, Li Wang, Li Xiao, Sijie Yu, Hongxing Chen, Fanghong Zhang, Mo Wang, Shaojun Li, Haiping Yang, Qiu Li","doi":"10.1186/s12911-025-03203-4","DOIUrl":"10.1186/s12911-025-03203-4","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"364"},"PeriodicalIF":3.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12505573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243790","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":"Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor).","authors":"Jennifer Jiang-Kells, James Brandreth, Leilei Zhu, Jack Ross, Yogini Jani, Enrico Costanza, Maisarah Amran, Zeljko Kraljevic, Xi Bai, M M N S Dilan, Jayathri Wijayarathne, Ravi Wickramaratne, Folkert W Asselbergs, Richard J B Dobson, Wai Keong Wong, Anoop D Shah","doi":"10.1186/s12911-025-03195-1","DOIUrl":"10.1186/s12911-025-03195-1","url":null,"abstract":"<p><strong>Background: </strong>Well-organised electronic health records (EHR) are essential for high quality patient care, but EHR user interfaces can be cumbersome for entry of structured information, resulting in the majority of information being in free text rather than a structured form. This makes it difficult to retrieve information for clinical purposes and limits the research potential of the data. Natural language processing (NLP) at the point of care has been suggested as a way of improving data quality and completeness, but there is little evidence as to its effectiveness. We sought to generate such evidence by developing an open source, modular, configurable NLP system called MiADE, which is designed to integrate with an EHR. This paper describes the design of MiADE and the deployment at University College London Hospitals (UCLH), and is intended to benefit those who may wish to develop or implement a similar system elsewhere.</p><p><strong>Results: </strong>The MiADE system includes components to extract diagnoses, medications and allergies from a clinical note, and communicate with an EHR system in real time using Health Level 7 Clinical Document Architecture (HL7 CDA) messaging. This enables NLP results to be displayed to a clinician for verification before saving them to the patient's record. MiADE utilises the MedCAT library (part of the Cogstack family of NLP tools) for named entity recognition (NER) and linking to SNOMED CT, as well as context detection. MedCAT models underwent unsupervised and supervised training on patient notes from UCLH, achieving precision of 83.2% (95% CI 77.0, 88.1), and recall of 85.2% (95% CI 79.1, 89.8) for detection of diagnosis concepts. In simulation testing we found that MiADE reduced the time taken for clinicians to enter structured problem lists by 89%. We have commenced a trial implementation of MiADE at UCLH in live clinical use, integrated with the Epic EHR at UCLH.</p><p><strong>Conclusions: </strong>We have developed an open source point of care NLP system and successfully integrated it with the EHR in live clinical use at a major hospital. Simulation testing has shown that our system significantly reduces the time taken for clinicians to enter structured diagnosis codes.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"365"},"PeriodicalIF":3.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12506320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243763","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":"AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization.","authors":"Mahmoud Badawy, Yousry AbdulAzeem, Hanaa ZainEldin, Hossam Magdy Balaha, Amna Bamaqa, Rasha F El-Agamy, Hanaa A Sayed, Mostafa A Elhosseini","doi":"10.1186/s12911-025-03133-1","DOIUrl":"10.1186/s12911-025-03133-1","url":null,"abstract":"<p><p>Bone marrow transplantation (BMT) is a critical treatment for various hematological diseases in children, offering a potential cure and significantly improving patient outcomes. However, the complexity of matching donors and recipients and predicting post-transplant complications presents significant challenges. In this context, machine learning (ML) and artificial intelligence (AI) serve essential functions in enhancing the analytical processes associated with BMT. This study introduces a novel Computer-Aided Diagnosis (CAD) framework that analyzes critical factors such as genetic compatibility and human leukocyte antigen types for optimizing donor-recipient matches and increasing the success rates of allogeneic BMTs. The CAD framework employs Particle Swarm Optimization for efficient feature selection, seeking to determine the most significant features influencing classification accuracy. This is complemented by deploying diverse machine-learning models to guarantee strong and adaptable analytical capabilities. The Adaptive Tree of Parzen Estimators (TPE), a Bayesian optimization technique, is a key component of the proposed methodology. TPE is instrumental in navigating the complex hyperparameter space to optimize model performance, enhancing the overall effectiveness of the ML algorithms. Besides, the study investigates the impact of various scaling techniques on model performance, including L1 normalization and L2 normalization, ensuring that data preprocessing is optimized for the best possible outcomes. The Local Interpretable Model-Agnostic Explanations (LIME) framework is utilized to enhance model transparency and interpretability, bridging the gap between complex AI algorithms and clinical usability. This study uses a comprehensive dataset titled \"Bone Marrow Transplant: Children, which is the analysis's foundation. The findings, validated by ANOVA and T-tests, reveal significant associations between several factors and survival status, highlighting the importance of Donorage, extcGvHD, PLTrecovery, and survival_time, among others. The optimal CAD framework employs a majority voting ensemble of seven finely-tuned machine learning algorithms, achieving remarkable performance metrics. The proposed CAD framework not only achieves high accuracy (98.07%), Balanced Accuracy (98.08%), precision (98.45%), recall (98.02%), specificity (98.14%), F1 score (98.23%), and Intersection over Union (96.53%) but also offers interpretable insights into the classification procedure, contributing significantly as a comprehensive tool for clinicians in the domain of childhood BMT.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"363"},"PeriodicalIF":3.8,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237950","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}