Articles
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Citation: BMC Medical Informatics and Decision Making 2023 23:112
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Development and analysis of quality assessment tools for different types of patient information – websites, decision aids, question prompt lists, and videos
Our working group has developed a set of quality assessment tools for different types of patient information material. In this paper we review and evaluate these tools and their development process over the pa...
Citation: BMC Medical Informatics and Decision Making 2023 23:111 -
Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine
Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative...
Citation: BMC Medical Informatics and Decision Making 2023 23:110 -
Correction to: Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary
Citation: BMC Medical Informatics and Decision Making 2023 23:109 -
Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients
Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE)...
Citation: BMC Medical Informatics and Decision Making 2023 23:108 -
Construction of the XGBoost model for early lung cancer prediction based on metabolic indices
Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cance...
Citation: BMC Medical Informatics and Decision Making 2023 23:107 -
Development and usability evaluation of a mHealth application for albinism self-management
Reduced or absence of melanin poses physical, social, and psychological challenges to individuals with albinism. Mobile health (mHealth) applications have the potential to improve the accessibility of informat...
Citation: BMC Medical Informatics and Decision Making 2023 23:106 -
Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study
Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine lea...
Citation: BMC Medical Informatics and Decision Making 2023 23:105 -
Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach
Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for pra...
Citation: BMC Medical Informatics and Decision Making 2023 23:104 -
Developing a mobile health application for wound telemonitoring: a pilot study on abdominal surgeries post-discharge care
Many early signs of Surgical Site Infection (SSI) developed during the first thirty days after discharge remain inadequately recognized by patients. Hence, it is important to use interactive technologies for p...
Citation: BMC Medical Informatics and Decision Making 2023 23:103 -
Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures
This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm.
Citation: BMC Medical Informatics and Decision Making 2023 23:102 -
Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques
This study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being ...
Citation: BMC Medical Informatics and Decision Making 2023 23:101 -
Digital communication and virtual reality for extending the behavioural treatment of obesity – the patients’ perspective: results of an online survey in Germany
CBT has been found effective for the treatment of EDs and obesity. However not all patients achieve clinically significant weight loss and weight regain is common. In this context, technology-based interventio...
Citation: BMC Medical Informatics and Decision Making 2023 23:100 -
Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning
Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention an...
Citation: BMC Medical Informatics and Decision Making 2023 23:99 -
Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia
The prevalence of end-stage renal disease has raised the need for renal replacement therapy over recent decades. Even though a kidney transplant offers an improved quality of life and lower cost of care than d...
Citation: BMC Medical Informatics and Decision Making 2023 23:98 -
Digital encounter decision aids linked to clinical practice guidelines: results from user testing SHARE-IT decision aids in primary care
Encounter decision aids (EDAs) are tools that can support shared decision making (SDM), up to the clinical encounter. However, adoption of these tools has been limited, as they are hard to produce, to keep up-...
Citation: BMC Medical Informatics and Decision Making 2023 23:97 -
An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy
Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic ep...
Citation: BMC Medical Informatics and Decision Making 2023 23:96 -
The tragic paradoxical effect of telemedicine on healthcare disparities- a time for redemption: a narrative review
Telemedicine has become more convenient and advantageous due to the rapid development of the internet and telecommunications. A growing number of patients are turning to telemedicine for health consultations a...
Citation: BMC Medical Informatics and Decision Making 2023 23:95 -
FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital
Secondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This...
Citation: BMC Medical Informatics and Decision Making 2023 23:94 -
Confidence-based laboratory test reduction recommendation algorithm
We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.
Citation: BMC Medical Informatics and Decision Making 2023 23:93 -
2.5D MFFAU-Net: a convolutional neural network for kidney segmentation
Kidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity a...
Citation: BMC Medical Informatics and Decision Making 2023 23:92 -
Development and usability evaluation of an electronic health report form to assess health in young people: a mixed-methods approach
Electronic Patient-Reported Outcomes (ePROs) have potential to improve health outcomes and healthcare. The development of health-technology applications, such as ePROs, should include the potential users and b...
Citation: BMC Medical Informatics and Decision Making 2023 23:91 -
Automated approach for quality assessment of RDF resources
The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-action...
Citation: BMC Medical Informatics and Decision Making 2023 23(Suppl 1):90 -
A drug recommender system for the treatment of hypertension
One third (20% to 30%) of patients suffering from hypertension show increased blood pressure resistant to treatment. This resistance often has multifactorial causes, like therapeutic inertia and inappropriate ...
Citation: BMC Medical Informatics and Decision Making 2023 23:89 -
Big knowledge visualization of the COVID-19 CIDO ontology evolution
The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious...
Citation: BMC Medical Informatics and Decision Making 2023 23(Suppl 1):88 -
Logical definition-based identification of potential missing concepts in SNOMED CT
Biomedical ontologies are representations of biomedical knowledge that provide terms with precisely defined meanings. They play a vital role in facilitating biomedical research in a cross-disciplinary manner. ...
Citation: BMC Medical Informatics and Decision Making 2023 23(Suppl 1):87 -
Ontology-driven and weakly supervised rare disease identification from clinical notes
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machi...
Citation: BMC Medical Informatics and Decision Making 2023 23:86 -
De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation
Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, ...
Citation: BMC Medical Informatics and Decision Making 2023 23:85 -
Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms
Polypharmacy (PP) is increasingly common in Iran, and contributes to the substantial burden of drug-related morbidity, increasing the potential for drug interactions and potentially inappropriate medications. ...
Citation: BMC Medical Informatics and Decision Making 2023 23:84 -
The development and phase 1 evaluation of a Decision Aid for elective egg freezing
Elective egg freezing decisions are complex. We developed a Decision Aid for elective egg freezing and conducted a phase 1 study to evaluate its acceptability and utility for decision-making.
Citation: BMC Medical Informatics and Decision Making 2023 23:83 -
MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model
Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex dise...
Citation: BMC Medical Informatics and Decision Making 2023 23:82 -
A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients
A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) tec...
Citation: BMC Medical Informatics and Decision Making 2023 23:81 -
Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory
Estimating the surgery length has the potential to be utilized as skill assessment, surgical training, or efficient surgical facility utilization especially if it is done in real-time as a remaining surgery du...
Citation: BMC Medical Informatics and Decision Making 2023 23:80 -
SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances
Clinical practices have demonstrated that disease treatment can be very complex. Patients with chronic diseases often suffer from more than one disease. Complex diseases are often treated with a variety of dru...
Citation: BMC Medical Informatics and Decision Making 2023 23:79 -
An early detection and segmentation of Brain Tumor using Deep Neural Network
Magnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care pl...
Citation: BMC Medical Informatics and Decision Making 2023 23:78 -
Pregnant women’s use and attitude toward Mobile phone features for self-management
This study aimed to examine the current use of mobile phones by pregnant women and their attitudes towards the use of a variety of prenatal care services through mHealth.
Citation: BMC Medical Informatics and Decision Making 2023 23:77 -
The fudan tinnitus relieving system application for tinnitus management
Tinnitus is a highly prevalent hearing disorder, and the burden of tinnitus diagnosis and treatment is very heavy, especially in China. In order to better benefit the majority of tinnitus patients, we develope...
Citation: BMC Medical Informatics and Decision Making 2023 23:76 -
Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022
Treatment with effective antiretroviral therapy (ART) reduces viral load as well as HIV-related morbidity and mortality in HIV-positive patients. Despite the expanded availability of antiretroviral therapy aro...
Citation: BMC Medical Informatics and Decision Making 2023 23:75 -
The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model
This research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model...
Citation: BMC Medical Informatics and Decision Making 2023 23:74 -
The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions
Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient...
Citation: BMC Medical Informatics and Decision Making 2023 23:73 -
Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region
Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machin...
Citation: BMC Medical Informatics and Decision Making 2023 23:72 -
Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm su...
Citation: BMC Medical Informatics and Decision Making 2023 23:71 -
Machine learning prediction of mortality in Acute Myocardial Infarction
Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients wi...
Citation: BMC Medical Informatics and Decision Making 2023 23:70 -
Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning
Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine ...
Citation: BMC Medical Informatics and Decision Making 2023 23:69 -
A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods
The incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different d...
Citation: BMC Medical Informatics and Decision Making 2023 23:68 -
Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality
Machine-learning models are susceptible to external influences which can result in performance deterioration. The aim of our study was to elucidate the impact of a sudden shift in covariates, like the one caus...
Citation: BMC Medical Informatics and Decision Making 2023 23:67 -
Exploring patient perspectives on the secondary use of their personal health information: an interview study
The increased digitalisation of health records has resulted in increased opportunities for the secondary use of health information for advancing healthcare. Understanding how patients want their health informa...
Citation: BMC Medical Informatics and Decision Making 2023 23:66 -
Accurate breast cancer diagnosis using a stable feature ranking algorithm
Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging.
Citation: BMC Medical Informatics and Decision Making 2023 23:64 -
Developing and testing the usability, acceptability, and future implementation of the Whole Day Matters Tool and User Guide for primary care providers using think-aloud, near-live, and interview procedures
Canada’s 24-Hour Movement Guidelines for Adults have shifted the focus from considering movement behaviours (i.e., physical activity, sedentary behaviour, and sleep) separately to a 24-h paradigm, which consid...
Citation: BMC Medical Informatics and Decision Making 2023 23:57 -
Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study
Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the bes...
Citation: BMC Medical Informatics and Decision Making 2023 23:54
Annual Journal Metrics
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2022 Citation Impact
3.5 - 2-year Impact Factor
3.9 - 5-year Impact Factor
1.384 - SNIP (Source Normalized Impact per Paper)
0.940 - SJR (SCImago Journal Rank)2023 Speed
37 days submission to first editorial decision for all manuscripts (Median)
213 days submission to accept (Median)2023 Usage
2,588,758 downloads
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Peer-review Terminology
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The following summary describes the peer review process for this journal:
Identity transparency: Single anonymized
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