BMC Medical Informatics and Decision Making最新文献

{"title":"Mortality predicting models for patients with infective endocarditis: a machine learning approach.","authors":"Yang Zi-Yang, Wang Qi, Xingyan Liu, Haolin Li, Shouhong Wang, Danqing Yu, Xuebiao Wei","doi":"10.1186/s12911-025-03025-4","DOIUrl":"10.1186/s12911-025-03025-4","url":null,"abstract":"<p><strong>Background: </strong>Infective endocarditis (IE) is a fatal cardiovascular disease with varied clinical manifestations but rapid progression. A series of existing risk models helped identify IE patients with high risk, but the imperfect predictive performance and limited application called for better predictive systems.</p><p><strong>Methods: </strong>The single-centered, retrospective observational study applied four machine learning methods for predictive model construction: LASSO logistic regression, random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN). A 10-fold cross-validated area under the receiver operating characteristic curve (AUC-ROC) was used for performance evaluation.</p><p><strong>Results: </strong>A total of 1705 patients with IE were enrolled in the study, with 119 in-hospital deaths and 178 deaths after 6-month follow-up. RF achieved the highest AUC-ROCs for in-hospital and six-month mortality prediction (in-hospital: 0.83, 6-month: 0.85). RF was also applied to assess variable importance. The following variables were selected by RF as top important predictors for both in-hospital and six-month mortality prediction: total bilirubin, N-terminal pro-B-type natriuretic peptide, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age.</p><p><strong>Conclusions: </strong>A risk model with machine learning approach was integrated in purpose of prognosis prediction in IE patients, helping rapid risk stratification and in-time management clinically.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"229"},"PeriodicalIF":3.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539037","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 deep learning model for predicting systemic lupus erythematosus-associated epitopes.","authors":"Jiale He, Zixia Liu, Xiaopo Tang","doi":"10.1186/s12911-025-03056-x","DOIUrl":"10.1186/s12911-025-03056-x","url":null,"abstract":"<p><strong>Background: </strong>The accurate prediction of epitopes associated with Systemic Lupus Erythematosus (SLE) plays a vital role in advancing our understanding of autoimmune pathogenesis and in designing effective immunotherapeutics. Traditional bioinformatics methods often struggle to capture the intricate sequence patterns and high-dimensional signals characteristic of epitope data. Deep learning presents a compelling alternative, with its ability to perform automatic feature learning and model complex dependencies inherent in biological sequences. This study proposes a hybrid deep learning architecture that synergistically integrates handcrafted biochemical features with data-driven deep sequence modeling to improve the identification of SLE-associated epitopes.</p><p><strong>Methods: </strong>The framework comprises six interconnected components: (1) handcrafted feature extraction encoding biochemical and physicochemical attributes; (2) an embedding layer for dense sequence representation; (3) a Convolutional Neural Network (CNN) branch that captures local patterns from handcrafted features; (4) a Long Short-Term Memory branch for learning temporal dependencies in sequence data; (5) a scaled dot-product attention-based fusion module that integrates complementary information from both branches; and (6) a Multi-Layer Perceptron for final classification. Model evaluation employed metrics such as Accuracy, Precision, Recall, F1-score, and the area under the receiver operating characteristic curve (ROCAUC).</p><p><strong>Results: </strong>The hybrid model outperformed both baseline machine learning algorithms and ablated versions of itself. It achieved a ROCAUC of 0.9506 and an F1-score of 0.8333 on the SLE epitope prediction task. Notably, ablation studies revealed that the CNN component had the most substantial influence on performance, while the custom fusion mechanism yielded better integration of features than conventional strategies. These findings underscore the model's robustness and capacity to generalize across complex epitope prediction tasks.</p><p><strong>Conclusion: </strong>This work presents an interpretable, biologically informed deep learning approach for predicting SLE-associated epitopes. By merging domain-specific handcrafted features with dynamic deep learning representations, the model not only enhances predictive accuracy but also provides meaningful biological insights. The framework holds promise for broader applications in immunoinformatics and autoimmune disease research.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"230"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539065","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":"Health information system in primary health care units of the Central Zone, Tigray, Northern Ethiopia.","authors":"Letekirstos Gebreegziabher Gebretsadik, Abate Bekele Belachew, Gebregziabher Berihu Gebrekidan, Alemayohu Bayray, Akberet Lemlem, Lewtnesh Berihun Dangew, Haftom Temesgen Abebe","doi":"10.1186/s12911-025-03078-5","DOIUrl":"10.1186/s12911-025-03078-5","url":null,"abstract":"<p><strong>Background: </strong>Health information systems require the management of health information through health management information systems and research and knowledge management. In many low-income countries, including Ethiopia, poor data quality and limited use of health information remain major challenges in the health system. Reliable health data quality is essential for evidence-based decision-making and improving quality health service delivery. This study aimed to assess and explore the contextual factors of the quality and utilization of health information in primary health care units in the Central Zone, Tigray, Northern Ethiopia.</p><p><strong>Methods: </strong>A facility-based cross-sectional quantitative and qualitative study design was used. A total of seven primary health care units and four district health offices were selected. Data were collected via document review, structured questionnaires and in-depth interviews. A three-month document review was conducted to assess data accuracy via lot quality assurance sampling. Forty-eight health professionals, including Woreda Health Office heads, facility heads, health management information system focal persons, service providers and health extension workers, were interviewed for quantitative analysis. Additionally, 23 key informants with the same roles participated in the qualitative interviews. Descriptive statistics were computed, and thematic analysis was conducted for the qualitative data.</p><p><strong>Results: </strong>Four of the seven primary health care units have assigned health management information system personnel, and five of them have necessary equipment for health management information systems. The average lot quality assurance sampling of the primary health care units ranged from 35 to 60%, which falls below the national threshold of 90% data accuracy. In knowing and measuring the dimensions of data quality, the informants described this as a difficult task despite acknowledging its importance. Similarly, the culture of data use for decision making was limited.</p><p><strong>Conclusion: </strong>This study revealed that primary health care units in the Central Zone of Tigray face significant challenges in terms of data quality and utilization, primarily due to the limited capacity of service providers, unclear understanding of data quality dimensions and weak data use culture. The average lot quality assurance sampling accuracy rates are below the acceptable level, indicating issues in the data documentation and validation processes. Addressing these gaps through targeted capacity-building, including the integration of HMIS curricula at the university level and system-level improvements such as implementing computerized systems, ensuring accountability and allocating budgets, is needed to strengthen health information systems and enable evidence-based decision-making at all levels of the health system.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"233"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539033","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":"Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a systematic review.","authors":"Joel Jia Wei Ng, Eugene Wang, Xinyan Zhou, Kevin Xiang Zhou, Charlene Xing Le Goh, Gabriel Zheng Ning Sim, Hiang Khoon Tan, Serene Si Ning Goh, Qin Xiang Ng","doi":"10.1186/s12911-025-03061-0","DOIUrl":"10.1186/s12911-025-03061-0","url":null,"abstract":"<p><strong>Background: </strong>Clinical documentation is vital for effective communication, legal accountability and the continuity of care in healthcare. Traditional documentation methods, such as manual transcription, are time-consuming, prone to errors and contribute to clinician burnout. AI-driven transcription systems utilizing automatic speech recognition (ASR) and natural language processing (NLP) aim to automate and enhance the accuracy and efficiency of clinical documentation. However, the performance of these systems varies significantly across clinical settings, necessitating a systematic review of the published studies.</p><p><strong>Methods: </strong>A comprehensive search of MEDLINE, Embase, and the Cochrane Library identified studies evaluating AI transcription tools in clinical settings, covering all records up to February 16, 2025. Inclusion criteria encompassed studies involving clinicians using AI-based transcription software, reporting outcomes such as accuracy (e.g., Word Error Rate), time efficiency and user satisfaction. Data were extracted systematically, and study quality was assessed using the QUADAS-2 tool. Due to heterogeneity in study designs and outcomes, a narrative synthesis was performed, with key findings and commonalities reported.</p><p><strong>Results: </strong>Twenty-nine studies met the inclusion criteria. Reported word error rates ranged widely, from 0.087 in controlled dictation settings to over 50% in conversational or multi-speaker scenarios. F1 scores spanned 0.416 to 0.856, reflecting variability in accuracy. Although some studies highlighted reductions in documentation time and improvements in note completeness, others noted increased editing burdens, inconsistent cost-effectiveness and persistent errors with specialized terminology or accented speech. Recent LLM-based approaches offered automated summarization features, yet often required human review to ensure clinical safety.</p><p><strong>Conclusions: </strong>AI-based transcription systems show potential to improve clinical documentation but face challenges in accuracy, adaptability and workflow integration. Refinements in domain-specific training, real-time error correction and interoperability with electronic health records are critical for their effective adoption in clinical practice. Future research should also focus on next-generation \"digital scribes\" incorporating LLM-driven summarization and repurposing of text.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"236"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539019","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":"Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.","authors":"Mehak Arshad, Muhammad Attique Khan, Nouf Abdullah Almujally, Areej Alasiry, Mehrez Marzougui, Yunyoung Nam","doi":"10.1186/s12911-025-03051-2","DOIUrl":"10.1186/s12911-025-03051-2","url":null,"abstract":"<p><strong>Background and objective: </strong>Early detection and classification of skin cancer are critical for improving patient outcomes. Dermoscopic image analysis using Computer-Aided Diagnostics (CAD) is a powerful tool to assist dermatologists in identifying and classifying skin lesions. Traditional machine learning models require extensive feature engineering, which is time-consuming and less effective in handling complex data like skin lesions. This study proposes a deep learning-based network-level fusion architecture that integrates multiple deep models to enhance the classification and localization of skin lesions in dermoscopic images. The goal is to address challenges like irregular lesion shapes, inter-class similarities, and class imbalances while providing explainability through artificial intelligence.</p><p><strong>Methods: </strong>A novel hybrid contrast enhancement technique was applied for pre-processing and dataset augmentation. Two deep learning models, a 5-block inverted residual network and a 6-block inverted bottleneck network, were designed and fused at the network level using a depth concatenation approach. The models were trained using Bayesian optimization for hyperparameter tuning. Feature extraction was performed with a global average pooling layer, and shallow neural networks were used for final classification. Explainable AI techniques, including LIME, were used to interpret model predictions and localize lesion regions. Experiments were conducted on two publicly available datasets, HAM10000 and ISIC2018, which were split into training and testing sets.</p><p><strong>Results: </strong>The proposed fused architecture achieved high classification accuracy, with results of 91.3% and 90.7% on the HAM10000 and ISIC2018 datasets, respectively. Sensitivity, precision, and F1-scores were significantly improved after data augmentation, with precision rates of up to 90.91%. The explainable AI component effectively localized lesion areas with high confidence, enhancing the model's interpretability.</p><p><strong>Conclusions: </strong>The network-level fusion architecture combined with explainable AI techniques significantly improved the classification and localization of skin lesions. The augmentation and contrast enhancement processes enhanced lesion visibility, while fusion of models optimized classification accuracy. This approach shows potential for implementation in CAD systems for skin cancer diagnosis, although future work is required to address the limitations of computational resource requirements and training time.</p><p><strong>Clinical trail number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"215"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539038","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":"Framework for key benchmarking indicators in hospital information system.","authors":"Asghar Ehteshami, Ahamad-Reza Raeisi, Maedeh Rashedi, Yasin Sabet Kouhanjani","doi":"10.1186/s12911-025-03038-z","DOIUrl":"10.1186/s12911-025-03038-z","url":null,"abstract":"<p><strong>Introduction: </strong>Developing key benchmarking indicators (KBIs) for Hospital Information Systems (HIS) has the potential to enhance operational efficiency and effectiveness, resulting in the successful achievement of hospital goals. This study endeavored to develop a comprehensive framework of KBIs for HIS to address the need for structured evaluation in healthcare systems.</p><p><strong>Methods: </strong>This qualitative study was conducted at Ahvaz Jundishapour University of Medical Sciences' academic hospitals. The study comprised two distinct phases. In the initial phase, data collection involved purposive sampling with a snowball technique, consisting of 14 semi-structured interviews with health information technology, medical informatics, and health information management experts. During the second phase, benchmarking indicators were extracted through conventional content analysis and prioritized using two rounds of the Delphi technique.</p><p><strong>Results: </strong>The study identified three main themes and eight sub-themes, including Technical (software, hardware, architecture and user interface, and outputs), Human and Organizational (vendors, IT support, and HIS workflows), and Financial (costs). A total of 76 KBIs were developed and prioritized, with key indicators such as user-friendliness and response time scoring 100% in importance, while others like cost-effectiveness and interoperability quality scored 96.4% and 98.8%, respectively.</p><p><strong>Conclusion: </strong>The proposed framework incorporates essential benchmarking indicators intended to enhance HIS efficiency, effectiveness, and successful attainment of its objectives. These indicators may serve as a comprehensive tool for Health Information Technology managers to benchmark their HISs. Practical implications include the potential for hospital administrators to identify gaps in system performance, optimize resource allocation, and improve user satisfaction by applying the KBIs. The framework may also guide decision-making processes, supporting alignment with organizational goals and promoting the long-term sustainability of HIS implementations.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"213"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539031","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 image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease.","authors":"Lijuan Zhu, Xiaomeng Shi, Lusong Tang, Haruhiko Machida, Lili Yang, Meixiang Ma, Ruoshui Ha, Yun Shen, Fang Wang, Dazhi Chen","doi":"10.1186/s12911-025-03049-w","DOIUrl":"10.1186/s12911-025-03049-w","url":null,"abstract":"<p><strong>Objective: </strong>Deep learning image reconstruction (DLIR) technology effectively improves the image quality while maintaining spatial resolution. The impact of DLIR on the quantification of coronary artery calcium (CAC) is still unclear. The purpose of this study was to investigate the effect of DLIR on the quantification of coronary calcium in high-risk populations.</p><p><strong>Methods: </strong>A retrospective study was conducted on patients who underwent coronary artery CT angiography (CCTA) at our hospital(China) from February 2022 to September 2022. Raw data were reconstructed with filtered back projection (FBP) reconstruction, 40% and 80% level adaptive statistical iterative reconstruction-veo (ASiR-V 40%, ASiR-V 80%) and low, medium and high-level deep learning algorithm (DLIR-L, DLIR-M, and DLIR-H). Calculate and compare the signal-to-noise and contrast-to-noise ratio, volumetric score, mass scores, and Agaston score of 6 sets of images.</p><p><strong>Results: </strong>There were 178 patients, female (107), mean age (62.43 ± 9.26), and mean BMI (25.33 ± 3.18) kg/m². Compared with FBP, the image noise of ASiR-V and DLIR was significantly reduced (P < 0.001). There was no significant difference in Agaston score, volumetric score, and mass scores among the six reconstruction algorithms (all P > 0.05). Bland-Altman diagram indicated that the Agatston scores of the five reconstruction algorithms showed good agreement with FBP, with DLIR-L(AUC, 110.08; 95% CI: 26.48, 432.92;)and ASIR-V40% (AUC,110.96; 95% CI: 26.23, 431.34;) having the highest consistency with FBP.</p><p><strong>Conclusion: </strong>Compared with FBP, DLIR and ASiR-V improve CT image quality to varying degrees while having no impact on Agatston score-based risk stratification.</p><p><strong>Clinical relevance statement: </strong>CACS is a powerful tool for cardiovascular risk stratification, and DLIR can improve image quality without affecting CACS, making it widely applicable in clinical practice.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"212"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539014","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 prediction model for hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke: a retrospective analysis.","authors":"Yidan Chen, Wendie Lv, Xuhui Liu, Mingmin Yan, Jing Zheng, Dan Yan, Dan Wang, Yulin Yao, Bingxi Liu, Yahui Li, Yue Wan","doi":"10.1186/s12911-025-03068-7","DOIUrl":"10.1186/s12911-025-03068-7","url":null,"abstract":"<p><strong>Background: </strong>Hemorrhagic transformation (HT) is a serious and common complication following intravenous thrombolysis in acute ischemic stroke (AIS), often leading to worsened outcomes. Identifying risk factors for HT and developing accurate predictive models are essential for improving patient management and prognosis.</p><p><strong>Methods: </strong>A retrospective analysis was performed on 159 patients with acute ischemic stroke who received intravenous thrombolytic therapy at Hubei Third People's Hospital Affiliated to Jianghan University School of Medicine from March 2019 to July 2022. Boruta algorithm and multivariable logistic regression analysis were used to identify independent factors associated with bleeding transformation. A nomogram was built based on these factors and internally verified using the bootstrap resampling method.</p><p><strong>Results: </strong>Our analysis showed that the independent factors affecting HT were Hyperdense middle cerebral artery sign (HMCAS), pre-thrombolytic glucose, pre-thrombolytic neutrophil count and construct a nomogram based on these predictors. The area under the ROC curve (AUC) of the line graph was 0.885 (95%CI = 0.816 ~ 0.953), and the calibration curve showed that the probability predicted by the line graph was in good agreement with the actual observed values. The ROC curve and decision curve analysis (DCA), which assesses clinical usefulness, showed that the nomogram provided greater net benefit than the three individual predictors.</p><p><strong>Conclusions: </strong>In this study, a static and dynamic online nomogram with good differentiation, calibration and accuracy was constructed to help identify high-risk patients before thrombolysis, help physicians make decisions and improve patient outcomes.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"227"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539017","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":"Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation parameters: an observational study.","authors":"Xin-Yue Song, Xin-Peng Xie, Wen-Jing Xu, Yu-Jia Cao, Bin-Miao Liang","doi":"10.1186/s12911-025-03064-x","DOIUrl":"10.1186/s12911-025-03064-x","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The integration of machine learning (ML) algorithms enables the detection of diffusion abnormalities-related respiratory changes in individuals with normal body mass index (BMI), overweight, and obesity based on BMI and pulmonary ventilation parameters. We evaluated the effectiveness of various supervised ML algorithms and identified the optimal configurations for these applications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective analysis of data from 440 individuals who underwent pulmonary function tests between January 1, 2021, and April 1, 2024. This cohort consisted of 287 individuals with normal diffusion capacity (DN) and 153 with diffusion abnormalities (DA). We employed statistical comparisons (e.g., independent samples t-test and Chi-square test) to analyze demographic characteristics and spirometry results. Piecewise regression evaluated the correlation between BMI and carbon monoxide diffusing capacity (DL&lt;sub&gt;CO&lt;/sub&gt;). Pulmonary ventilation parameters included forced vital capacity (FVC), forced expiratory volume in one second (FEV&lt;sub&gt;1&lt;/sub&gt;), FEV&lt;sub&gt;1&lt;/sub&gt;/FVC, peak expiratory flow (PEF), maximum mid-expiratory flow (MMEF) and vital capacity (VC). We applied several supervised ML algorithms and feature selection strategies to distinguish between DN and DA, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Naive Bayes (BAYES), K-Nearest Neighbors (KNN), SelectKBest, Recursive Feature Elimination with Cross-Validation (RFECV), and SelectFromModel. Additionally, we performed feature importance analysis using shapley additive explanations (SHAP) and permutation importance to evaluate the contribution of individual parameters to the classification process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our findings revealed that individuals in the DA group demonstrated lower PEF and DL&lt;sub&gt;CO&lt;/sub&gt; than their DN counterparts. BMI displayed a cubic relationship with DL&lt;sub&gt;CO&lt;/sub&gt; for 18.5 kg/m² &lt; BMI &lt; 40 kg/m² (R² = 0.498, P &lt; 0.01), and a linear negative correlation for BMI ≥ 40 kg/m² (r = -0.253, P &lt; 0.05). Notably, the RF algorithm emerged as the most effective diagnostic tool for distinguishing between DN and DA, achieving an area under the curve (AUC) of 0.983, considerably outpacing other algorithms like BAYES, SVM, AdaBoost, and KNN (P &lt; 0.01). Applying various feature selection strategies identified optimal parameters (BMI, FEV&lt;sub&gt;1&lt;/sub&gt;/FVC, and VC) in subsequent experiments, which aligned with the results from feature importance analysis and pulmonary physiology. While feature selection enhanced KNN's diagnostic accuracy, it had a minimal impact on BAYES's performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The results indicate that for individuals with a BMI between 18.5 kg/m² and 40 kg/m², diffusion capacity improves with increasing BMI. Conversely, diffusion capacity decreases for those with a BMI of 40 kg/m² or higher. This study underscores the pote","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"240"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539035","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":"AMPDECIDE amputation level patient decision aids: a feasibility study.","authors":"Alison W Henderson, Maryam Soltani, Bjoern D Suckow, Alison R Kern, Daniel D Matlock, Joseph M Czerniecki, Daniel C Norvell","doi":"10.1186/s12911-025-03084-7","DOIUrl":"10.1186/s12911-025-03084-7","url":null,"abstract":"<p><strong>Objective: </strong>This was a feasibility study of the AMPDECIDE amputation level selection patient decision aids (one transmetatarsal vs. transtibial, the other transtibial vs. transfemoral) designed to inform a larger efficacy trial. We intended to gather data about usability of the aids, gather efficacy data about an amputation-level specific knowledge scale, identify any patient-barriers to the use of the decision aids, and evaluate the feasibility of our study methods.</p><p><strong>Design: </strong>Feasibility study with an uncontrolled before-after design in two medical centers.</p><p><strong>Methods: </strong>A convenience sample of dysvascular patients (both pre- and post-amputation) seen by either the vascular or orthopaedic surgery services at each facility were recruited. Enrolled patients completed baseline measures (including amputation level knowledge items). They then reviewed the decision aid with a research coordinator, followed by additional measures of control preference, numeracy, literacy and open-ended questions.</p><p><strong>Results: </strong>Eleven patients were enrolled (9-post amputation, 2 pre-amputation). Patients rated the decision aids as easy to navigate. Nearly all patients expressed a desire to see their personalized mobility and reamputation risks should they be made available. Patients demonstrated 17% improved amputation level knowledge after exposure to the decision aids. In addition, 81% of patients indicated wanting to participate in the amputation level decision. The study encountered difficulties identifying and recruiting patients until greater clinician involvement was included.</p><p><strong>Conclusions: </strong>The AMPDECIDE patient decision aids and the study measures appear well suited for a larger efficacy trial. Patients were able to digest the information supplied in the aids and responded well to them. The initial recruitment strategy was insufficient; greater clinician involvement may help in the future.</p><p><strong>Clinical trial number: </strong>Not applicable.</p><p><strong>Trial registration: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"218"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538991","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}