BMC Medical Informatics and Decision Making最新文献

{"title":"Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.","authors":"Min Fang, Chengjie Pan, Xiaoyi Yu, Wenjuan Li, Ben Wang, Huajian Zhou, Zhenying Xu, Genyuan Yang","doi":"10.1186/s12911-025-02956-2","DOIUrl":"10.1186/s12911-025-02956-2","url":null,"abstract":"<p><p>Hyperuricemia has seen a continuous increase in incidence and a trend towards younger patients in recent years, posing a serious threat to human health and highlighting the urgency of using technological means for disease risk prediction. Existing risk prediction models for hyperuricemia typically include two major categories of indicators: routine blood tests and biochemical tests. The potential of using routine blood tests alone for prediction has not yet been explored. Therefore, this paper proposes a hyperuricemia risk prediction model that integrates Particle Swarm Optimization (PSO) with machine learning, which can accurately assess the risk of hyperuricemia by relying solely on routine blood data. In addition, an interpretability method based on Explainable Artificial Intelligence(XAI) is introduced to help medical staff and patients understand how the model makes decisions. This paper uses Cohen's d value to compare the differences in indicators between hyperuricemia and non-hyperuricemia patients and identifies risk factors through multivariate logistic regression. Subsequently, a risk prediction model is constructed based on the parameter optimization of five machine learning models using the PSO algorithm. The accuracy and sensitivity of the proposed particle swarm fusion Stacking model reach 97.8% and 97.6%, marking an improvement in accuracy of over 11% compared to the state-of-the-art models. Finally, a sensitivity analysis of factors affecting the prediction results is conducted using the XAI method. This paper has also developed a Health Portrait Platform that integrates the proposed risk prediction models, enabling real-time online health risk assessment. Since only routine blood test data are used, the new model has better feasibility and scalability, providing a valuable reference for assessing the risk of hyperuricemia occurrence.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"131"},"PeriodicalIF":3.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11910002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633742","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 based model for the early detection of Gestational Diabetes Mellitus.","authors":"Hesham Zaky, Eleni Fthenou, Luma Srour, Thomas Farrell, Mohammed Bashir, Nady El Hajj, Tanvir Alam","doi":"10.1186/s12911-025-02947-3","DOIUrl":"10.1186/s12911-025-02947-3","url":null,"abstract":"<p><strong>Background: </strong>Gestational Diabetes Mellitus (GDM) is one of the most common medical complications during pregnancy. In the Gulf region, the prevalence of GDM is higher than in other parts of the world. Thus, there is a need for the early detection of GDM to avoid critical health conditions in newborns and post-pregnancy complexities of mothers.</p><p><strong>Methods: </strong>In this article, we propose a machine learning (ML)-based techniques for early detection of GDM. For this purpose, we considered clinical measurements taken during the first trimester to predict the onset of GDM in the second trimester.</p><p><strong>Results: </strong>The proposed ensemble-based model achieved high accuracy in predicting the onset of GDM with around 89% accuracy using only the first trimester data. We confirmed biomarkers, i.e., a history of high glucose level/diabetes, insulin and cholesterol, which align with the previous studies. Moreover, we proposed potential novel biomarkers such as HbA1C %, Glucose, MCH, NT pro-BNP, HOMA-IR- (22.5 Scale), HOMA-IR- (405 Scale), Magnesium, Uric Acid. C-Peptide, Triglyceride, Urea, Chloride, Fibrinogen, MCHC, ALT, family history of Diabetes, Vit B12, TSH, Potassium, Alk Phos, FT4, Homocysteine Plasma LC-MSMS, Monocyte Auto.</p><p><strong>Conclusion: </strong>We believe our findings will complement the current clinical practice of GDM diagnosis at an early stage of pregnancy, leading toward minimizing its burden on the healthcare system.Source code is available in GitHub at: https://github.com/H-Zaky/GD.git.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"130"},"PeriodicalIF":3.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623583","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":"Pseudonymization tools for medical research: a systematic review.","authors":"Hammam Abu Attieh, Armin Müller, Felix Nikolaus Wirth, Fabian Prasser","doi":"10.1186/s12911-025-02958-0","DOIUrl":"10.1186/s12911-025-02958-0","url":null,"abstract":"<p><strong>Background: </strong>Pseudonymization is an important technique for the secure and compliant use of medical data in research. At its core, pseudonymization is a process in which directly identifying information is separated from medical research data. Due to its importance, a wide range of pseudonymization tools and services have been developed, and researchers face the challenge of selecting an appropriate tool for their research projects. This review aims to address this challenge by systematically comparing existing tools.</p><p><strong>Methods: </strong>A systematic review was performed and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines where applicable. The search covered PubMed and Web of Science to identify pseudonymization tools documented in the scientific literature. The tools were assessed based on predefined criteria across four key dimensions that describe researchers' requirements: (1) single-center vs. multi-center use, (2) short-term vs. long-term projects, (3) small data vs. big data processing, and (4) integration vs. standalone functionality.</p><p><strong>Results: </strong>From an initial pool of 1,052 papers, 92 were selected for detailed full-text review after the title and abstract screening. This led to the identification of 20 pseudonymization tools, of which 10 met our inclusion criteria and were assessed. The results show that there are differences between the tools that make them more or less suited for research projects differing in regards to the dimensions described above, enabling us to provide targeted recommendations.</p><p><strong>Conclusions: </strong>The landscape of existing pseudonymization tools is heterogeneous, and researchers need to carefully select the appropriate solutions for their research projects. Our findings highlight two Software-as-a-Service-based solutions that enable centralized use without local infrastructure, one tool for retrospective pseudonymization of existing databases, two tools suitable for local deployment in smaller, short-term projects, and two tools well-suited for local deployment in large, multi-center studies.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"128"},"PeriodicalIF":3.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613488","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":"Parallel privacy preservation through partitioning (P4): a scalable data anonymization algorithm for health data.","authors":"Mehmed Halilovic, Thierry Meurers, Karen Otte, Fabian Prasser","doi":"10.1186/s12911-025-02959-z","DOIUrl":"10.1186/s12911-025-02959-z","url":null,"abstract":"<p><strong>Background: </strong>Sharing health data holds great potential for advancing medical research but also poses many challenges, including the need to protect people's privacy. One approach to address this is data anonymization, which refers to the process of altering or transforming a dataset to preserve the privacy of the individuals contributing data. To this, privacy models have been designed to measure risks and optimization algorithms can be used to transform data to achieve a good balance between risks reduction and the preservation of the dataset's utility. However, this process is computationally complex and challenging to apply to large datasets. Previously suggested parallel algorithms have been tailored to specific risk models, utility models and transformation methods.</p><p><strong>Methods: </strong>We present a novel parallel algorithm that supports a wide range of methods for measuring risks, optimizing utility and transforming data. The algorithm trades data utility for parallelization, by anonymizing partitions of the dataset in parallel. To ensure the correctness of the anonymization process, the algorithm carefully controls the process and if needed rearranges partitions and performs additional transformations.</p><p><strong>Results: </strong>We demonstrate the effectiveness of our method through an open-source implementation. Our experiments show that our approach can reduce execution times by up to one order of magnitude with minor impacts on output data utility in a wide range of scenarios.</p><p><strong>Conclusions: </strong>Our novel P4 algorithm for parallel and distributed data anonymization is, to the best of our knowledge, the first to systematically support a wide variety of privacy, transformation and utility models.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"129"},"PeriodicalIF":3.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613486","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":"Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?","authors":"Guixiong Huang, Weilin Zhu, Yulong Wang, Yizhou Wan, Kaifang Chen, Yanlin Su, Weijie Su, Lianxin Li, Pengran Liu, Xiao Dong Guo","doi":"10.1186/s12911-025-02943-7","DOIUrl":"10.1186/s12911-025-02943-7","url":null,"abstract":"<p><strong>Objective: </strong>This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, evaluating its performance, and conducting validations to explore the most suitable machine learning algorithm.</p><p><strong>Methods: </strong>Clinical information, including demographic data, examination results, medical history, and laboratory test results, was collected from inpatients with and without osteoporosis. The LASSO algorithm was utilized for feature selection, and multiple machine learning algorithms were applied to calculate the model's accuracy, precision, recall, F1 score, and average precision (AP) value. Receiver operating characteristic (ROC) curves for each algorithm were plotted, and a comprehensive evaluation was conducted to identify the most suitable machine learning model. Finally, the model's predictive accuracy was validated using corresponding information from other patients.</p><p><strong>Results: </strong>A total of 1063 patients were included; 562 had osteoporosis, and 501 did not. After LASSO feature selection, the most important features for the model's predictive results were determined to be age, height, weight, alkaline phosphatase activity, and osteocalcin. Evaluation of the accuracy, precision, recall, F1 score, and AP value for each algorithm, along with ROC curves, led to the selection of the light gradient boosting machine (LGBM) algorithm as the best algorithm for the model. The validation results confirmed the model's excellent predictive ability.</p><p><strong>Conclusion: </strong>This study established a preliminary diagnostic model for osteoporosis, contributing to increased efficiency in diagnosing the disease.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"127"},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11898998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603965","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":"Risk-based evaluation of machine learning-based classification methods used for medical devices.","authors":"Martin Haimerl, Christoph Reich","doi":"10.1186/s12911-025-02909-9","DOIUrl":"10.1186/s12911-025-02909-9","url":null,"abstract":"<p><strong>Background: </strong>In the future, more medical devices will be based on machine learning (ML) methods. In general, the consideration of risks is a crucial aspect for evaluating medical devices. Accordingly, risks and their associated costs should be taken into account when assessing the performance of ML-based medical devices. This paper addresses the following three research questions towards a risk-based evaluation with a focus on ML-based classification models.</p><p><strong>Methods: </strong>First, we analyzed how often risk-based metrics are currently utilized in the context of ML-based classification models. This was performed using a literature research based on a sample of recent scientific publications. Second, we introduce an approach for evaluating such models where expected risks and associated costs are integrated into the corresponding performance metrics. Additionally, we analyze the impact of different risk ratios on the resulting overall performance. Third, we elaborate how such risk-based approaches relate to regulatory requirements in the field of medical devices. A set of use case scenarios were utilized to demonstrate necessities and practical implications, in this regard.</p><p><strong>Results: </strong>First, it was shown that currently most scientific publications do not include risk-based approaches for measuring performance. Second, it was demonstrated that risk-based considerations have a substantial impact on the outcome. The relative increase of the resulting overall risks can go up to 196% when the ratio between different types of risks (false negatives vs. false positives) changes by a factor of 10.0. Third, we elaborated that risk-based considerations need to be included into the assessment of ML-based medical devices, according to the relevant EU regulations and standards. In particular, this applies when a substantial impact on the clinical outcome / in terms of the risk-benefit relationship occurs.</p><p><strong>Conclusion: </strong>In summary, we demonstrated the necessity of a risk-based approach for the evaluation of medical devices which include ML-based classification methods. We showed that currently many scientific papers in this area do not include risk considerations. We developed basic steps towards a risk-based assessment of ML-based classifiers and elaborated consequences that could occur, when these steps are neglected. And, we demonstrated the consistency of our approach with current regulatory requirements in the EU.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"126"},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603931","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":"Power asymmetry and embarrassment in shared decision-making: predicting participation preference and decisional conflict.","authors":"Karin Antonia Scherer, Björn Büdenbender, Anja K Blum, Britta Grüne, Maximilian C Kriegmair, Maurice S Michel, Georg W Alpers","doi":"10.1186/s12911-025-02938-4","DOIUrl":"10.1186/s12911-025-02938-4","url":null,"abstract":"<p><strong>Background: </strong>Shared decision-making (SDM) is the gold standard for patient-clinician interaction, yet many patients are not actively involved in medical consultations and hesitate to engage in decisions on their health. Despite considerable efforts to improve implementation, research on barriers to SDM within the patient-clinician relationship and interaction is scant. To identify potential barriers to urological patients' participation in decision-making, we developed two novel scales assessing power asymmetry (PA-ME) and embarrassment in medical encounters (EmMed). The present study validates both scales in a large sample comprising urological patients and non-clinical participants. It further examines the effects of both factors on participation preferences and decisional conflict among patients.</p><p><strong>Methods: </strong>Data were collected from 107 urological patients at a university hospital for Urology and Urosurgery in Germany. Patients completed self-report questionnaires before and after their clinical appointments. In addition, 250 non-clinical participants provided data via an online study. All participants rated perceived power asymmetry in the patient-clinician relationship and their experience of embarrassment in medical contexts using the PA-ME and EmMed scales. Urological patients further indicated their participation preference in decisions regarding both general and urological care prior to the consultation. Afterward, they assessed the level of perceived decisional conflict.</p><p><strong>Results: </strong>Factor analyses yielded power asymmetry and medical embarrassment as unidimensional constructs. Both questionnaires have good (PA-ME; α = 0.88), respectively excellent (EmMed; α = 0.95), internal consistency. Among urological patients, higher levels of perceived power asymmetry predicted lower generic participation preference (β = - 0.98, p <.001, adjusted R² = 0.14) and higher decisional conflict (β = 0.25, p <.01, adjusted R² = 0.07). While, in patients, embarrassment was not linked to generic participation preference before the consultation (p ≥.5), it resulted in higher decisional conflict after the consultation (β = 0.39, p <.001, adjusted R² = 0.14). Neither power asymmetry nor embarrassment were specifically associated with participation preference regarding urological care (p ≥.273).</p><p><strong>Conclusions: </strong>Given their promising psychometric properties, the new instruments are recommended for routine assessment of power asymmetry and embarrassment among patients. Addressing these factors may be helpful to reduce decisional conflict and increase participation preferences. Both factors are prerequisites for a successful SDM-process and active patient engagement in health-related decisions.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"120"},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596347","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 generic decision guide for patients in oncology: a qualitative interview study.","authors":"Lia Schilling, Jana Kaden, Isabel Bán, Birte Berger-Höger","doi":"10.1186/s12911-025-02960-6","DOIUrl":"10.1186/s12911-025-02960-6","url":null,"abstract":"<p><strong>Background: </strong>Many patients with cancer want to be involved in healthcare decisions. For adequate participation, awareness of one's own desires and preferences and sufficient knowledge about medical measures are indispensable. In order to support patient participation, a decision guide for patients with cancer was developed as part of a larger project called TARGET, which specifically aims to improve the care of patients with rare cancer.</p><p><strong>Methods: </strong>The development of the decision guide took place from 08.2022 to 03.2023. The decision guide is a single component of a complex intervention that aims to facilitate decision support in cancer care for patients. For the development, existing development and evaluation studies of Question Prompt Lists (QPLs) were identified through systematic literature searches in the MEDLINE via PubMed, PsycInfo, and CINAHL databases. The decision guide was pre-tested for feasibility, usability, completeness and acceptance with the target groups through guided individual interviews. Sociodemographic data were collected anonymously. An expert review was conducted. The verbatim transcribed interviews were analysed using content analysis according to Kuckartz with MAXQDA. The guide has been iteratively optimized based on the results.</p><p><strong>Results: </strong>A generic decision guide for patients with cancer for diagnostic or treatment decisions was developed in both PDF web-based formats, based on the Ottawa Personal Decision Guide. It was supplemented with decision-related questions from QPLs for patients with cancer. The pre-test comprised seven expert reviews of (psych)oncologists and experts in evidence-based health information and ten interviews with cancer patients (n = 7), family relatives (n = 2), and one caregiver. The results were coded into nine main categories. The results indicated a good feasibility, usability and acceptability of the guide. The tool was perceived as comprehensive and appropriate. Individual elements were identified as modifiable for better comprehensibility. The target audience appreciated the decision guide as a good support option.</p><p><strong>Conclusion: </strong>The decision guide is potentially a useful support option for patients with cancer facing medical decisions in their further course of treatment. In the TARGET project, it will be made available to patients and can be supplemented with decision coaching. Further steps for implementation into healthcare structures are necessary.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"125"},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596341","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":"An intelligent multi-attribute decision-making system for clinical assessment of spinal cord disorder using fuzzy hypersoft rough approximations.","authors":"Muhammad Abdullah, Khuram Ali Khan, Atiqe Ur Rahman","doi":"10.1186/s12911-025-02946-4","DOIUrl":"10.1186/s12911-025-02946-4","url":null,"abstract":"<p><p>The data for diagnosing spinal cord disorder (SCD) are complex and often confusing, making it difficult for established diagnostic techniques to yield reliable results. This issue frequently necessitates expensive testing to get an accurate diagnosis. However, the diagnostic process can be enhanced by integrating theoretical frameworks that resemble fuzzy sets, which better manage complexity and uncertainty. This integration reduces the frequency of expensive diagnostic procedures, improving the effectiveness of decision-making. The goal of this work is to present lower and upper approximations for fuzzy hypersoft sets, which employ multi-argument-based parameters to improve the traditional lower and upper approximations of fuzzy sets and soft sets. An intelligent mechanism for decision assistance is established by proposing a robust algorithm, that is based on the proposed approximations. To validate the proposed algorithm, a prototype case study for the clinical diagnosis of SCD is discussed. The criteria are further refined by using pertinent sub-criteria, such as functional ability, imaging data, and neurological status criteria. Medical professionals would find the suggested approximations to be a very helpful tool as the results indicate that they could greatly improve diagnosis. This study contributes to the field of medical diagnostics by providing a sophisticated multi-criteria analytical tool that can manage the complexity and inherent ambiguity of SCD diagnosis.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"122"},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596298","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":"Comparing conventional and Bayesian workflows for clinical outcome prediction modelling with an exemplar cohort study of severe COVID-19 infection incorporating clinical biomarker test results.","authors":"Brian Sullivan, Edward Barker, Louis MacGregor, Leo Gorman, Philip Williams, Ranjeet Bhamber, Matt Thomas, Stefan Gurney, Catherine Hyams, Alastair Whiteway, Jennifer A Cooper, Chris McWilliams, Katy Turner, Andrew W Dowsey, Mahableshwar Albur","doi":"10.1186/s12911-025-02955-3","DOIUrl":"10.1186/s12911-025-02955-3","url":null,"abstract":"<p><strong>Purpose: </strong>Assessing risk factors and creating prediction models from real-world medical data is challenging, requiring numerous modelling decisions with clinical guidance. Logistic regression is a common model for such studies, for which we advocate the use of Bayesian methods that can jointly deliver probabilistic risk factor inference and prediction. As an exemplar, we compare Bayesian logistic regression with horseshoe priors and Projective Prediction variable selection with the established frequentist LASSO approach, to predict severe COVID-19 outcomes (death or ICU admittance) from demographic and laboratory biomarker data. Our study serves as guidance on data curation, variable selection, and performance assessment with cross-validation.</p><p><strong>Methods: </strong>Our source data is based on a retrospective observational cohort design with records from three National Health Service (NHS) Trusts in southwest England, UK. Models were fit to predict severe outcomes within 28 days after admission to hospital (or a positive PCR result if already admitted) using demographic data and the first result from 30 biomarker tests collected within 3 days after admission (or testing positive if already admitted).</p><p><strong>Results: </strong>Patients included hospitalized adults positive for COVID-19 from March to October 2020, 756 total patients: Mean age 71, 45% female, 31% (n=234) had a severe outcome, of whom 88% (n=206) died. Patients were split into training (n=534) and external validation groups (n=222). Using our Bayesian pipeline, we show a reduced variable model using Age, Urea, Prothrombin time (PT) C-reactive protein (CRP), and Neutrophil-Lymphocyte ratio (NLR) has better predictive performance (median external AUC: 0.71, 95% Quantile [0.7, 0.72]) relative to a GLM using all variables (external AUC: 0.67 [0.63, 0.71]).</p><p><strong>Conclusion: </strong>Urea, PT, CRP, and NLR have been highlighted by other studies, and respectively suggest that hypovolemia, derangement of circulation via clotting, and inflammation are strong predictive risk factors of severity. This study provides guidance on conventional and Bayesian regression and prediction modelling with complex clinical data.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"123"},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596335","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}