BMC Medical Informatics and Decision Making最新文献

{"title":"Development and evaluation of a clinical nursing decision support system for the prevention of neonatal hypoglycaemia.","authors":"Qiaoyan Liu, Lulu Sun, Jie Yang, Wei Yin, Songmei Cao","doi":"10.1186/s12911-024-02826-3","DOIUrl":"10.1186/s12911-024-02826-3","url":null,"abstract":"<p><strong>Background: </strong>Hypoglycaemia is one of the most common complications during the neonatal period. Recurrent hypoglycaemia episodes can result in neurodevelopmental deficits and even sudden death. Available evidence indicates that healthcare professionals ought to promptly assess the risk of hypoglycaemia in newborns immediately following birth and formulate the most suitable preventive strategies. Consequently, this study was designed to develop a clinical nursing decision support system for neonatal hypoglycaemia prevention based on the prediction model for neonatal hypoglycaemia risk that was developed in a previous study, and to evaluate its efficacy.</p><p><strong>Methods: </strong>Nursing process as the theoretical framework, based on evidence-based nursing, standardized nursing language, and clinical decision support technology, the neonatal hypoglycaemia prevention nursing decision support system was developed.This system was implemented in the neonatology department of a tertiary grade A general hospital from September 1st to 30th, 2023.The application efficacy of the system was assessed and compared through the examination of the incidence of neonatal hypoglycemia, adverse outcomes associated with neonatal hypoglycemia, and the experiences of nurses following the implementation of the system.</p><p><strong>Results: </strong>The incidence of neonatal hypoglycaemia decreased after the system was implemented, and the difference was statistically significant (X² = 4.522, P = 0.033). None of the neonates experienced adverse outcomes during hospitalization. The rate of hypoglycaemia risk assessment in neonates after system implementation was 92.16%. The total Clinical Nursing Information System Effectiveness Evaluation Scale score was 104.36 ± 1.96.</p><p><strong>Conclusion: </strong>The neonatal hypoglycaemia prevention nursing decision support system realizes neonatal hypoglycaemia risk assessment, intelligent decision-making, and effect evaluation, effectively diminishes the incidence of neonatal hypoglycaemia, and enhances the standardization of neonatal hypoglycaemia management.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"400"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881394","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":"Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV).","authors":"Panpan Chen, Junhua Sun, Yingjie Chu, Yujie Zhao","doi":"10.1186/s12911-024-02829-0","DOIUrl":"10.1186/s12911-024-02829-0","url":null,"abstract":"<p><strong>Background: </strong>Heart failure (HF) and atrial fibrillation (AF) usually coexist and are associated with a poorer prognosis. This study aimed to develop a model to predict in-hospital mortality in patients with HF combined with AF.</p><p><strong>Methods: </strong>Patients with HF and AF were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database from 2008 to 2019. Feature selection was based on the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model. Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN) models, and their stacked model (the stacking ensemble model) were established. The area under of the curve (AUC) with 95% confidence interval (CI), sensitivity, specificity, as well as accuracy were applied to assess the performance of the predictive models.</p><p><strong>Results: </strong>A total of 5,998 patients with HF combined with AF were included, of which 4,198 patients were assigned to the training set and 1,800 to the testing set (7:3). Among these 4,198 patients, 624 (14.86%) died in-hospital and 3,574 (85.14%) survived. Twenty-two features were used to construct the predictive model. Among these four single models, the AUC was 0.747 (95%CI: 0.717-0.777) for the Random Forest model, 0.755 (95%CI: 0.725-0.785) for the XGBoost model, 0.754 (95%CI: 0.724-0.784) for the LGBM model, and 0.746 (95%CI: 0.716-0.776) for the KNN model in the testing set. The stacking ensemble model had the highest AUC compared to the four single models, with AUCs of 0.837 (95%CI: 0.821-0.852) and 0.768 (95%CI: 0.740-0.796) for the training set and testing set, respectively.</p><p><strong>Conclusion: </strong>The stacking ensemble model showed a good predictive effect in predicting in-hospital mortality in patients with HF combined with AF and may provide clinicians with a reference tool for early identification of mortality risk.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"402"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881404","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":"Automated redaction of names in adverse event reports using transformer-based neural networks.","authors":"Eva-Lisa Meldau, Shachi Bista, Carlos Melgarejo-González, G Niklas Norén","doi":"10.1186/s12911-024-02785-9","DOIUrl":"10.1186/s12911-024-02785-9","url":null,"abstract":"<p><strong>Background: </strong>Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports. The target domain for this study was case narratives from the United Kingdom's Yellow Card scheme, which collects and monitors information on suspected side effects to medicines and vaccines.</p><p><strong>Methods: </strong>We finetuned BERT - a transformer-based neural network - for recognising names in case narratives. Training data consisted of newly annotated records from the Yellow Card data and of the i2b2 2014 deidentification challenge. Because the Yellow Card data contained few names, we used predictive models to select narratives for training. Performance was evaluated on a separate set of annotated narratives from the Yellow Card scheme. In-depth review determined whether (parts of) person names missed by the de-identification method could enable re-identification of the individual, and whether de-identification reduced the clinical utility of narratives by collaterally masking relevant information.</p><p><strong>Results: </strong>Recall on held-out Yellow Card data was 87% (155/179) at a precision of 55% (155/282) and a false-positive rate of 0.05% (127/ 263,451). Considering tokens longer than three characters separately, recall was 94% (102/108) and precision 58% (102/175). For 13 of the 5,042 narratives in Yellow Card test data (71 with person names), the method failed to flag at least one name token. According to in-depth review, the leaked information could enable direct identification for one narrative and indirect identification for two narratives. Clinically relevant information was removed in less than 1% of the 5,042 processed narratives; 97% of the narratives were completely untouched.</p><p><strong>Conclusions: </strong>Automated redaction of names in free-text narratives of adverse event reports can achieve sufficient recall including shorter tokens like patient initials. In-depth review shows that the rare leaks that occur tend not to compromise patient confidentiality. Precision and false positive rates are acceptable with almost all clinically relevant information retained.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"401"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881334","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 software tool for applying Bayes' theorem in medical diagnostics.","authors":"Theodora Chatzimichail, Aristides T Hatjimihail","doi":"10.1186/s12911-024-02721-x","DOIUrl":"10.1186/s12911-024-02721-x","url":null,"abstract":"<p><strong>Background: </strong>In medical diagnostics, estimating post-test or posterior probabilities for disease, positive and negative predictive values, and their associated uncertainty is essential for patient care.</p><p><strong>Objective: </strong>The aim of this work is to introduce a software tool developed in the Wolfram Language for the parametric estimation, visualization, and comparison of Bayesian diagnostic measures and their uncertainty.</p><p><strong>Methods: </strong>This tool employs Bayes' theorem to estimate positive and negative predictive values and posterior probabilities for the presence and absence of a disease. It estimates their standard sampling, measurement, and combined uncertainty, as well as their confidence intervals, applying uncertainty propagation methods based on first-order Taylor series approximations. It employs normal, lognormal, and gamma distributions.</p><p><strong>Results: </strong>The software generates plots and tables of the estimates to support clinical decision-making. An illustrative case study using fasting plasma glucose data from the National Health and Nutrition Examination Survey (NHANES) demonstrates its application in diagnosing diabetes mellitus. The results highlight the significant impact of measurement uncertainty on Bayesian diagnostic measures, particularly on positive predictive value and posterior probabilities.</p><p><strong>Conclusion: </strong>The software tool enhances the estimation and facilitates the comparison of Bayesian diagnostic measures, which are critical for medical practice. It provides a framework for their uncertainty quantification and assists in understanding and applying Bayes' theorem in medical diagnostics.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"399"},"PeriodicalIF":3.3,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and alpha-testing of a patient decision aid for patients with chronic myeloid leukemia regarding dose reduction.","authors":"D N Lokhorst, M F Djodikromo, R P M G Hermens, N M A Blijlevens, C L Bekker","doi":"10.1186/s12911-024-02806-7","DOIUrl":"10.1186/s12911-024-02806-7","url":null,"abstract":"<p><strong>Background: </strong>Dose reduction of tyrosine kinase inhibitors (TKIs) is an option for some chronic myeloid leukemia (CML) patients to minimize side effects while maintaining efficacy. Shared decision-making (SDM) and patient decision aids (PDAs) are advocated to make informed choices such as reducing the dose of TKIs. This paper describes the development and alpha-testing of a PDA for patients with CML receiving TKI dose reduction.</p><p><strong>Methods: </strong>The PDA was iteratively developed following IPDAS guidelines. First, a needs assessment with semi-structured interviews was conducted to understand the needs and preferences of patients and healthcare providers. Second, through feedback cycles with the project team and steering group the scope, content, and format were defined. Third, three rounds of alpha-testing were performed via individual \"think aloud\" sessions with patients (round 1) and healthcare providers (round 2) to qualitatively assess the comprehensibility, acceptability, and desirability of the PDA. Round 3 included quantitative evaluation via an acceptability and usability questionnaire. Qualitative data were categorized, and quantitative data were descriptively analyzed.</p><p><strong>Results: </strong>The majority valued the development of the PDA during the needs assessment (n = 30). The PDA included disease and treatment information, information about dose reduction, knowledge questions, and a value clarification section. During alpha-testing, the PDA was considered clear, balanced, and helpful for decision-making. A total of 76% of the patients (n = 17) and 100% of the healthcare providers (n = 9) recommended it with overall mean scores of 7.4 and 7.8, respectively. The above average usability score was 68.1.</p><p><strong>Conclusion: </strong>A well-accepted online PDA for chronic phase CML patients to consider TKI dose reduction was developed.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"398"},"PeriodicalIF":3.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11660603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871192","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":"Digital monitoring of medication safety in children: an investigation of ADR signalling techniques in Malaysia.","authors":"Beldona Hema Rekha, Shairyzah Ahmad Hisham, Izyan A Wahab, Norleen Mohamed Ali, Khang Wen Goh, Long Chiau Ming","doi":"10.1186/s12911-024-02801-y","DOIUrl":"10.1186/s12911-024-02801-y","url":null,"abstract":"<p><strong>Background: </strong>Digital solutions can help monitor medication safety in children who are often excluded in clinical trials. The lack of reliable safety data often leads to either under- or over-dose of medications during clinical management which make them either not responding well to treatment or susceptible to adverse drug reactions (ADRs).</p><p><strong>Aim: </strong>This study investigated ADR signalling techniques to detect serious ADRs in Malaysian children aged from birth to 12 years old using an electronic ADRs' database.</p><p><strong>Methods: </strong>Four techniques (Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS)) were tested on ADR reports submitted to the National Pharmaceutical Regulatory Agency between 2016 and 2020. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of the techniques were compared.</p><p><strong>Results: </strong>A total of 31 medicine-Important Medical Event pairs were found and examined among the 3152 paediatric ADR reports. Three techniques (PRR, ROR, MGPS) signalled oculogyric crisis and dystonia for metoclopramide. BCPNN and MGPS signalled angioedema for paracetamol, amoxicillin and ibuprofen. Similar performances were found for PRR, ROR and BCPNN (sensitivity of 12%, specificity of 100%, PPV of 100% and NPV of 21%). MGPS revealed the highest sensitivity (20%) and NPV (23%), as well as similar specificity and PPV (100%).</p><p><strong>Conclusions: </strong>This study suggests that medication safety signalling techniques could be applied on electronic health records to monitor medication safety issues in children. Clinicians and medication safety specialist could prioritise the signals for further clinical consideration and prompt response.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"395"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852890","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":"Uncertainty-aware automatic TNM staging classification for [¹⁸F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.","authors":"Stephen H Barlow, Sugama Chicklore, Yulan He, Sebastien Ourselin, Thomas Wagner, Anna Barnes, Gary J R Cook","doi":"10.1186/s12911-024-02814-7","DOIUrl":"10.1186/s12911-024-02814-7","url":null,"abstract":"<p><strong>Background: </strong>[¹⁸F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text. Accordingly, we developed a multi-task 'TNMu' classifier to classify the presence/absence of tumour, node, metastasis ('TNM') findings (as defined by The Eight Edition of TNM Staging for Lung Cancer). This is combined with an uncertainty classification task ('u') to account for studies with ambiguous TNM status.</p><p><strong>Methods: </strong>2498 reports were annotated by a nuclear medicine physician and split into train, validation, and test datasets. For additional evaluation an external dataset (n = 461 reports) was created, and annotated by two nuclear medicine physicians with agreement reached on all examples. We trained and evaluated eleven publicly available PLMs to determine which is most effective for PET-CT reports, and compared multi-task, single task and traditional machine learning approaches.</p><p><strong>Results: </strong>We find that a multi-task approach with GatorTron as PLM achieves the best performance, with an overall accuracy (all four tasks correct) of 84% and a Hamming loss of 0.05 on the internal test dataset, and 79% and 0.07 on the external test dataset. Performance on the individual TNM tasks approached expert performance with macro average F1 scores of 0.91, 0.95 and 0.90 respectively on external data. For uncertainty an F1 of 0.77 is achieved.</p><p><strong>Conclusions: </strong>Our 'TNMu' classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"396"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853031","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 potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.","authors":"Yuxiang Qi, Xu Liu, Zhishan Ding, Ying Yu, Zhenchao Zhuang","doi":"10.1186/s12911-024-02781-z","DOIUrl":"10.1186/s12911-024-02781-z","url":null,"abstract":"<p><strong>Background: </strong>Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by reduced hematological triad. It is challenging to distinguish and diagnose these two diseases. Hence, utilizing machine learning methods, we employed and validated an algorithm that used cell population data (CPD) parameters to diagnose AA and MDS.</p><p><strong>Methods: </strong>In this study, CPD parameters were obtained from the Beckman Coulter DxH800 analyzer for 160 individuals diagnosed with AA or MDS through a comprehensive retrospective analysis. The individuals were unselectively assigned to a training cohort (77%) and a testing cohort (23%). Additionally, an external validation cohort consisting of eighty-six elderly patients with AA and MDS from two additional centers was established. The discriminative parameters were carefully analyzed through univariate analysis, and the most predictive variables were selected using least absolute shrinkage and selection operator (LASSO) regression. Six machine learning algorithms were utilized to compare the performance of forecasting AA and MDS patients. The area under the curves (AUCs), calibration curves, decision curves analysis (DCA), and shapley additive explanations (SHAP) plots were employed to interpret and assess the model's predictive accuracy, clinical utility, and stability.</p><p><strong>Results: </strong>After the comparative evaluation of various models, the logistic regression model emerged as the most suitable machine learning model for predicting the probability of AA and MDS, which utilized five principal variables (age, MNVLY, SDVLY, MNLALSEGC, and MNCEGC) to accurately estimate the risk of these diseases. The best model delivered an AUC of 0.791 in the testing cohort and had a high specificity (0.850) and positive predictive value (0.818). Furthermore, the calibration curve indicated excellent agreement between actual and predicted probabilities. The DCA curve further supported the clinical utility of our model and offered significant clinical advantages in guiding treatment decisions. Moreover, the model's performance was consistent in an external validation group, with an AUC of 0.719.</p><p><strong>Conclusions: </strong>We developed a novel model that effectively distinguished elderly patients with AA and MDS, which had the potential to provide physicians assistance in early diagnosis and the proper treatment for the elderly.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"379"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853021","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 prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.","authors":"Xia Yu, Zi Yang, Xinzhuo Wang, Xiaoyu Sun, Ruiting Shen, Hongru Li, Mingchen Zhang","doi":"10.1186/s12911-024-02761-3","DOIUrl":"10.1186/s12911-024-02761-3","url":null,"abstract":"<p><p>Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing early warning methods struggle to extract crucial information comprehensively from complex multi-source heterogeneous data. In this paper, a deep learning framework with an innovative dynamic attention mechanism is proposed to predict nocturnal hypoglycemic events for type 1 diabetes patients. Features related to nocturnal hypoglycemia are extracted from multi-scale and multi-dimensional data, which enables comprehensive information extraction from diverse sources. Then, we propose a prior-knowledge-guided attention mechanism to enhance the network's learning capability and interpretability. The method was evaluated on a public available clinical dataset, which successfully warned 94.91% of nocturnal hypoglycemic events with an F1-score of 96.35%. By integrating our proposed framework into the nocturnal hypoglycemia early warning model, issues related to feature redundancy and incompleteness were mitigated. Comparative analysis demonstrates that our method outperforms existing approaches, offering superior accuracy and practicality in real-world scenarios.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"378"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853022","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":"The perception of facilitators and barriers to the use of e-health solutions in Poland: a qualitative study.","authors":"Paulina Smoła, Iwona Młoźniak, Monika Wojcieszko, Urszula Zwierczyk, Mateusz Kobryn, Elżbieta Rzepecka, Mariusz Duplaga","doi":"10.1186/s12911-024-02791-x","DOIUrl":"10.1186/s12911-024-02791-x","url":null,"abstract":"<p><strong>Background: </strong>E-health entails the use of information and communication technologies in support of health and health-related activities. E-health increased significantly during the COVID-19 pandemic in Poland. The pandemic showed that the e-health environment may be an important element of the response to epidemiological challenges. Polish citizens were provided with an array of e-health tools supporting the provision of health services.</p><p><strong>Methods: </strong>The main aim of the study was to assess the knowledge, use, and opinions about e-health solutions in Polish society. Fifty participants representing the general population took part in in-depth interviews. The interviews were conducted face-to-face with participants in their homes or via a teleconferencing platform from November 2023 to January 2024. At first, the interviewees were recruited by convenience, and at a later stage, a snowballing approach was applied. A semi-structured guide covered the knowledge about and use of e-health solutions, attitudes toward new technologies, and opinions about artificial intelligence and robots in healthcare. The interviewers interviewed 50 participants, of whom 26 were females. The interview transcriptions were analyzed with MAXQDA Analytics Pro 2022 (Release 22.7.0). An approach based on thematic analysis was employed to evaluate the interviews' content.</p><p><strong>Results: </strong>Thematic analysis of the interviews resulted in the identification of three main themes: (1) knowledge about e-health, (2) barriers, and (3) facilitators of e-health use. Recognition of the term 'e-health' was limited among study participants, although they used e-health solutions frequently. The main barriers included limited digital skills and unfavorable attitudes to new technologies. Some of the participants complained about technical difficulties, e.g., poor Internet access. The main facilitators identified based on the interviews include saving time and reducing costs, as well as the ability to access medical records in one repository, as in the case of the Internet Patient Account. Some people believed e-health to be an element of progress. Overall, the study participants supported sharing their medical data for research.</p><p><strong>Conclusions: </strong>Implementing e-health solutions seems to be perceived as an inevitable consequence of technological progress. However, a lack of adequate technical skills remains one of the major obstacles to efficiently utilizing e-health's potential.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"381"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853029","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}