BMC Medical Informatics and Decision Making最新文献

{"title":"A national platform for advancing self-care processes for the most common illnesses and conditions: designing, evaluating, and implementing.","authors":"Khadijeh Moulaei, Somayeh Salehi, Masoud Shahabian, Babak Sabet, Farshid Rezaei, Adrina Habibzadeh, Mohammad Reza Afrash","doi":"10.1186/s12911-024-02744-4","DOIUrl":"10.1186/s12911-024-02744-4","url":null,"abstract":"<p><strong>Background: </strong>Effective self-care practices are crucial for maintaining health and well-being, as inadequate self-care can lead to increased health risks and decreased overall quality of life. To address these issues, one promising approach involves leveraging progressive web app (PWA) platforms to educate and empower individuals with necessary self-care services. This study aims to design, implement, and evaluate a national self-care PWA platform, aiming to enhance accessibility and effectiveness in promoting health and self-care practices. The platform designed to improve self-care processes can be utilized by mothers, children, adolescents, youth, adults, and patients with emotional and mental disorders.</p><p><strong>Methods: </strong>This study was conducted in three phases. In the first phase, during 35 meetings with 19 health care providers including physicians and another group of professionals, the most common illnesses and conditions that require self-care were identified. Platform capabilities were then assessed based on stakeholder opinions. Subsequently, during 15 meetings 19 health care providers identified a comprehensive list of conditions benefiting from dedicated decision aids to enhance individuals' self-care processes. In the second phase, a progressive web app platform was designed based on these common illnesses and conditions and capabilities and subsequently evaluated. To usability evaluation the platform, 26 evaluators utilized the system for two weeks. The QUIS 5.5 questionnaire was employed for evaluation, and the results were analyzed using SPSS 23. In the final phase, the system was implemented at the Smart University of Medical Sciences (SMUMS), affiliated with the Ministry of Health and Medical Education in Iran.</p><p><strong>Results: </strong>Based on the most common illnesses and conditions (n = 87) and identified capabilities, the national self-care platform was designed with eight sections catering to 'Maternal and child health services,' 'Mothers,' 'Infants,' 'Teenagers,' 'Adults,' 'Elderly,' 'Health of All Age Groups,' 'Patients with Mental and Emotional Health Disorders,' and 'General Information' for user education. Furthermore, the platform features 54 decision aids (DA), teleconsultation services, and a self-care magazine (Access link: https://khodmoragheb.ir/ ). These features were integrated to provide comprehensive support and resources for self-care. A mean exceeding 7 was attained across all evaluated dimensions, indicating that evaluators generally agreed the platform performed well.</p><p><strong>Conclusion: </strong>The designed national self-care platform offers a promising solution for managing healthcare challenges. This innovative approach addresses the specific needs of individuals and extends its reach to Persian-speaking patients worldwide, fostering a global impact. By embracing self-care practices on an international scale, this platform contributes to a more inclusive a","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589396","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":"Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis.","authors":"Qili Yu, Zhiyong Hou, Zhiqian Wang","doi":"10.1186/s12911-024-02734-6","DOIUrl":"10.1186/s12911-024-02734-6","url":null,"abstract":"<p><strong>Background: </strong>In older adults with hypertension, hip fractures accompanied by preoperative acute heart failure significantly elevate surgical risks and adverse outcomes, necessitating timely identification and management to improve patient outcomes.</p><p><strong>Research objective: </strong>This study aims to enhance the early recognition of acute heart failure in older hypertensive adults prior to hip fracture surgery by developing a predictive model using logistic regression (LR) and machine learning methods, optimizing preoperative assessment and management.</p><p><strong>Methods: </strong>Employing a retrospective study design, we analyzed hypertensive older adults who underwent hip fracture surgery at Hebei Medical University Third Hospital from January 2018 to December 2022. Predictive models were constructed using LASSO regression and multivariable logistic regression, evaluated via nomogram charts. Five additional machine learning methods were utilized, with variable importance assessed using SHAP values and the impact of key variables evaluated through multivariate correlation analysis and interaction effects.</p><p><strong>Results: </strong>The study included 1,370 patients. LASSO regression selected 18 key variables, including sex, age, coronary heart disease, pulmonary infection, ventricular arrhythmias, acute myocardial infarction, and anemia. The logistic regression model demonstrated robust performance with an AUC of 0.753. Although other models outperformed it in sensitivity and F1 score, logistic regression's discriminative ability was significant for clinical decision-making. The Gradient Boosting Machine model, notable for a sensitivity of 95.2%, indicated substantial capability in identifying patients at risk, crucial for reducing missed diagnoses.</p><p><strong>Conclusion: </strong>We developed and compared efficacy of predictive models using logistic regression and machine learning, interpreting them with SHAP values and analyzing key variable interactions. This offers a scientific basis for assessing preoperative heart failure risk in older adults with hypertension and hip fractures, providing significant guidance for individualized treatment strategies and underscoring the value of applying machine learning in clinical settings.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589405","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":"Experiences and needs of older patients with stroke in China involved in rehabilitation decision-making: a qualitative study.","authors":"Zining Guo, Sining Zeng, Keyu Ling, Shufan Chen, Ting Yao, Haihan Li, Ling Xu, Xiaoping Zhu","doi":"10.1186/s12911-024-02735-5","DOIUrl":"10.1186/s12911-024-02735-5","url":null,"abstract":"<p><strong>Background: </strong>Shared decision-making is recommended for stroke rehabilitation. However, the complexity of the rehabilitation modalities exposes patients to decision-making conflicts, exacerbates their disabilities, and diminishes their quality of life. This study aimed to explore the experiences and needs of older patients with stroke in China during rehabilitation decision-making, providing a reference for developing decision-support strategies.</p><p><strong>Methods: </strong>A qualitative phenomenological design was used to explore the experiences and needs of older patients with stroke in China. Purposive sampling was used to recruit 31 older Chinese patients with stroke. The participants participated in face-to-face, semi-structured, and in-depth interviews. Data were analyzed using inductive thematic analysis.</p><p><strong>Results: </strong>The key themes identified include (1) mixed feelings in shared decision-making, (2) multiple barriers hinder the possibility of participating in shared decision-making, (3) Delegating rehabilitation decisions to surrogates, (4) gaps between reality and expectation, and (5) decision fatigue from lack of continuity in the rehabilitation health care system.</p><p><strong>Conclusions: </strong>Older patients with stroke in China have complex rehabilitation decision-making experiences and needs and face multiple obstacles when participating in shared decision-making. They lack an effective shared decision-making support system to assist them. Providing patients with comprehensive support (such as emotional and informational), strengthening the construction of a continuous rehabilitation system, alleviating economic pressure, and promoting patient participation in rehabilitation decision-making are necessary.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589400","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":"Analysis and knowledge extraction of newborn resuscitation activities from annotation files.","authors":"Mohanad Abukmeil, Øyvind Meinich-Bache, Trygve Eftestøl, Siren Rettedal, Helge Myklebust, Thomas Bailey Tysland, Hege Ersdal, Estomih Mduma, Kjersti Engan","doi":"10.1186/s12911-024-02736-4","DOIUrl":"10.1186/s12911-024-02736-4","url":null,"abstract":"<p><p>Deprivation of oxygen in an infant during and after birth leads to birth asphyxia, which is considered one of the leading causes of death in the neonatal period. Adequate resuscitation activities are performed immediately after birth to save the majority of newborns. The primary resuscitation activities include ventilation, stimulation, drying, suction, and chest compression. While resuscitation guidelines exist, little research has been conducted on measured resuscitation episodes. Objective data collected for measuring and registration of the executed resuscitation activities can be used to generate temporal timelines. This paper is primarily aimed to introduce methods for analyzing newborn resuscitation activity timelines, through visualization, aggregation, redundancy and dimensionality reduction. We are using two datasets: 1) from Stavanger University Hospital with 108 resuscitation episodes, and 2) from Haydom Lutheran Hospital with 76 episodes. The resuscitation activity timelines were manually annotated, but in future work we will use the proposed method on automatically generated timelines from video and sensor data. We propose an encoding generator with unique codes for combination of activities. A visualization of aggregated episodes is proposed using sparse nearest neighbor graph, shown to be useful to compare datasets and give insights. Finally, we propose a method consisting of an autoencoder trained for reducing redundancy in encoded resuscitation timeline descriptions, followed by a neighborhood component analysis for dimensionality reduction. Visualization of the resulting features shows very good class separability and potential for clustering the resuscitation files according to the outcome of the newborns as dead, admitted to NICU or normal. This shows great potential for extracting important resuscitation patterns when tested on larger datasets.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582333","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":"Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.","authors":"Xiaojing Li, Yueqin Tian, Shuangmei Li, Haidong Wu, Tong Wang","doi":"10.1186/s12911-024-02741-7","DOIUrl":"10.1186/s12911-024-02741-7","url":null,"abstract":"<p><strong>Background: </strong>Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors.</p><p><strong>Methods: </strong>This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. Further, the study employed multivariable Cox regression analysis and Kaplan-Meier survival curves, along with subgroup analysis, to explore the relationship between the machine learning-based score and 30-day mortality.</p><p><strong>Results: </strong>Through LASSO regression and recursive feature elimination (RFE), 25 optimal feature variables are selected. The XGBoost model performed best, with an area under the curve (AUC) of 0.881, a sensitivity of 0.5714, a specificity of 0.9651 and an F1 score of 0.64. The first six most important feature variables were the use of vasopressor, high Charlson comorbidity index, low blood oxygen saturation, history of malignant tumor, hyperglycemia and high APSIII score. Based on the optimal threshold of 0.62, patients were divided into high and low-risk groups, and the 30-day survival rate in the high-risk group decreased significantly. COX regression analysis further confirmed the positive correlation between high-risk scores and 30-day mortality. In the subgroup analysis, the model showed good risk stratification ability in patients with different gender, renal replacement therapy and with or without a history of malignant tumor, but it was not effective in predicting peripheral vascular disease.</p><p><strong>Conclusions: </strong>the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582353","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":"Adaptive questionnaires for facilitating patient data entry in clinical decision support systems: methods and application to STOPP/START v2.","authors":"Lamy Jean-Baptiste, Mouazer Abdelmalek, Léguillon Romain, Lelong Romain, Darmoni Stéfan, Sedki Karima, Dubois Sophie, Falcoff Hector","doi":"10.1186/s12911-024-02742-6","DOIUrl":"10.1186/s12911-024-02742-6","url":null,"abstract":"<p><p>Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter a lot of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for determining the relationships between rules and translating the system's clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire, both on clinical cases and real patient data. Presented to clinicians during focus group sessions, the adaptive questionnaire was found \"pretty easy to use\". In the future, this approach could be applied to other guidelines, and adapted for data entry by patients.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582329","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":"Feasibility of automated surveillance of implantable devices in orthopaedics via clinical data warehouse: the Studio study.","authors":"Marie Ansoborlo, Christine Salpétrier, Louis-Romé Le Nail, Julien Herbet, Marc Cuggia, Philippe Rosset, Leslie Grammatico-Guillon","doi":"10.1186/s12911-024-02697-8","DOIUrl":"10.1186/s12911-024-02697-8","url":null,"abstract":"<p><strong>Background: </strong>Total hip, knee and shoulder arthroplasties (THKSA) are increasing due to expanding demands in ageing population. Material surveillance is important to prevent severe complications involving implantable medical devices (IMD) by taking appropriate preventive measures. Automating the analysis of patient and IMD features could benefit physicians and public health policies, allowing early issue detection and decision support. The study aimed to demonstrate the feasibility of automated cohorting of patients with a first arthroplasty in two hospital data warehouses (HDW) in France.</p><p><strong>Methods: </strong>The study included adult patients with an arthroplasty between 2010 and 2019 identified by 2 data sources: hospital discharge and pharmacy. Selection was based on the health insurance thesaurus of IMDs in the pharmacy database: 1,523 distinct IMD references for primary THSKA. In the hospital discharge database, 22 distinct procedures for native joint replacement allowing a matching between IMD and surgical procedure of each patient selected. A program to automate information extraction was implemented in the 1st hospital data warehouse using natural language processing (NLP) on pharmacy labels, then it was then applied to the 2nd hospital.</p><p><strong>Results: </strong>The e-cohort was built with a first arthroplasty for THKSA performed in 7,587 patients with a mean age of 67.4 years, and a sex ratio of 0.75. The cohort involved 4,113 hip, 2,630 knee and 844 shoulder surgical patients. Obesity, cardio-vascular diseases and hypertension were the most frequent medical conditions.</p><p><strong>Discussion: </strong>The implementation of an e-cohort for material surveillance will be easily workable over HDWs France wild. Using NLP as no international IMD mapping exists to study IMD, our approach aims to close the gap between conventional epidemiological cohorting tools and bigdata approach.</p><p><strong>Conclusion: </strong>This pilot study demonstrated the feasibility of an e-cohort of orthopaedic devices using clinical data warehouses. The IMD and patient features could be studied with intra-hospital follow-up and will help analysing the infectious and unsealing complications.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11533334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575013","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":"Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters.","authors":"Jingru Huang, Yong Li, Xiaotan Pan, Jixiu Wei, Qiongqian Xu, Yin Zheng, Peng Chen, Jiabo Chen","doi":"10.1186/s12911-024-02716-8","DOIUrl":"10.1186/s12911-024-02716-8","url":null,"abstract":"<p><strong>Background: </strong>Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT.</p><p><strong>Methods: </strong>Four gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset.</p><p><strong>Results: </strong>Thirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data.</p><p><strong>Conclusion: </strong>This research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575003","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 scoping review, novel taxonomy and catalogue of implementation frameworks for clinical decision support systems.","authors":"Jared M Wohlgemut, Erhan Pisirir, Rebecca S Stoner, Zane B Perkins, William Marsh, Nigel R M Tai, Evangelia Kyrimi","doi":"10.1186/s12911-024-02739-1","DOIUrl":"10.1186/s12911-024-02739-1","url":null,"abstract":"<p><strong>Background: </strong>The primary aim of this scoping review was to synthesise key domains and sub-domains described in existing clinical decision support systems (CDSS) implementation frameworks into a novel taxonomy and demonstrate most-studied and least-studied areas. Secondary objectives were to evaluate the frequency and manner of use of each framework, and catalogue frameworks by implementation stage.</p><p><strong>Methods: </strong>A scoping review of Pubmed, Scopus, Web of Science, PsychInfo and Embase was conducted on 12/01/2022, limited to English language, including 2000-2021. Each framework was categorised as addressing one or multiple stages of implementation: design and development, evaluation, acceptance and integration, and adoption and maintenance. Key parts of each framework were grouped into domains and sub-domains.</p><p><strong>Results: </strong>Of 3550 titles identified, 58 papers were included. The most-studied implementation stage was acceptance and integration, while the least-studied was design and development. The three main framework uses were: for evaluating adoption, for understanding attitudes toward implementation, and for framework validation. The most frequently used framework was the Consolidated Framework for Implementation Research.</p><p><strong>Conclusions: </strong>Many frameworks have been published to overcome barriers to CDSS implementation and offer guidance towards successful adoption. However, for co-developers, choosing relevant frameworks may be a challenge. A taxonomy of domains addressed by CDSS implementation frameworks is provided, as well as a description of their use, and a catalogue of frameworks listed by the implementation stages they address. Future work should ensure best practices for CDSS design are adequately described, and existing frameworks are well-validated. An emphasis on collaboration between clinician and non-clinician affected parties may help advance the field.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564048","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":"Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI.","authors":"Shihua Gao, Haoran Zhu, Moshan Wen, Wei He, Yufeng Wu, Ziqi Li, Jiewei Peng","doi":"10.1186/s12911-024-02722-w","DOIUrl":"10.1186/s12911-024-02722-w","url":null,"abstract":"<p><strong>Background: </strong>Femoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipeline for ONFH based on magnetic resonance imaging (MRI) radiomics.</p><p><strong>Methods: </strong>In the segmentation model development dataset, T1-weighted MRI of 222 hips from two hospitals were retrospectively collected and randomly split into training (n = 190) and test (n = 32) sets. In the prognosis prediction model development dataset, 206 hips were also retrospectively collected from two hospitals and divided into training set (n = 155) and external test set (n = 51) according to data source. A deep learning model for automatic lesion segmentation was trained with nnU-Net, from which three-dimensional regions of interest were segmented and a total of 107 radiomics features were extracted. After intra-class correlation coefficients screening, feature correlation coefficient screening and Least Absolute Shrinkage and Selection Operator regression feature selection, a machine learning model for ONFH prognosis prediction was trained with Logistic Regression (LR) and Light Gradient Boosting Machine (LightGBM) algorithm.</p><p><strong>Results: </strong>The segmentation model achieved an average dice similarity coefficient of 0.848 and an average 95% Hausdorff distance of 3.794 in the test set, compared to the manual segmentation results. After feature selection, nine radiomics features were included in the prognosis prediction model. External test showed that the LightGBM model exhibited acceptable predictive performance. The area under the curve (AUC) of the prediction model was 0.851 (95% CI: 0.7268-0.9752), with an accuracy of 0.765, sensitivity of 0.833, and specificity of 0.727. Decision curve analysis showed that the LightGBM model exhibited favorable clinical utility.</p><p><strong>Conclusion: </strong>This study presents an automated pipeline for predicting femoral head collapse in ONFH with acceptable performance. Further research is necessary to determine the clinical applicability of this radiomics-based approach and to assess its potential to assist in treatment decision-making for ONFH.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557230","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}