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Deep learning models have been widely used in electroencephalogram (EEG) analysis and obtained excellent performance. But the adversarial attack and defense for them should be thoroughly studied before putting...
Respondent-driven sampling (RDS) refers both to a chain-referral sampling method and an analytical model for analysing sampled data. Web-based respondent-driven sampling (webRDS) uses internet-based recruitmen...
The original article was published in BMC Medical Informatics and Decision Making 2023 23:84
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...
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...
The original article was published in BMC Medical Informatics and Decision Making 2022 22:261
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)...
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...
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...
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...
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...
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...
This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm.
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 ...
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...
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...
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...
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-...
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...
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...
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...
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.
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...
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...
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...
This article is part of a Supplement: Volume 23 Supplement 1
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 ...
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...
This article is part of a Supplement: Volume 23 Supplement 1
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. ...
This article is part of a Supplement: Volume 23 Supplement 1
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...
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, ...
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. ...
The Correction to this article has been published in BMC Medical Informatics and Decision Making 2023 23:112
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.
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...
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...
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...
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...
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...
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.
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...
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...
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...
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...
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...
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...
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...
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 ...
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...
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...
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...
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.
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)
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37 days submission to first editorial decision for all manuscripts (Median)
213 days submission to accept (Median)
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