Page 4 of 77
Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of ar...
Antidepressants are a primary treatment for depression, yet prescribing them poses significant challenges due to the absence of clear guidelines for selecting the most suitable option for individual patients. ...
Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events...
This study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment.
In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students’ diagnostic abilities through interactive patient interviews (simulated pati...
The growing importance of mobile apps in osteoporosis management highlights the crucial need for evaluating their utility and usability, particularly for Osteoporosis support apps. Addressing this need, the mH...
Healthcare providers (HCP) face various stressful conditions in hospitals that result in the development of anxiety disorders. However, due to heavy workloads, they often miss the opportunity for self-care. An...
Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, fr...
This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.
As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Con...
This article is part of a Supplement: Volume 22 Supplement 5
This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review and random-effect meta-analy...
Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of...
Effective diagnostic capacity is crucial for clinical decision-making, with up to 70% of decisions in high-resource settings based on laboratory test results. However, in low- and middle-income countries (LMIC...
In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain...
The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health ...
Molecular tumor boards (MTBs) play a pivotal role in personalized oncology, leveraging complex data sets to tailor therapy for cancer patients. The integration of digital support and visualization tools is ess...
Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased ris...
Understanding the causal relationships between clinical outcomes and environmental exposures is critical for advancing public health interventions and personalized medicine. These causal relationships can be a...
The Correction to this article has been published in BMC Medical Informatics and Decision Making 2025 25:78
Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral h...
The ‘Ottawa Depression Algorithm’ is an evidence-based online tool developed to support primary care professionals care for adults with depression. Uptake of such tools require provider behaviour change. Ident...
Environmental exposures such as airborne pollutant exposures and socio-economic indicators are increasingly recognized as important to consider when conducting clinical research using electronic health record ...
The Correction to this article has been published in BMC Medical Informatics and Decision Making 2025 25:102
Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA.
The original article was published in BMC Medical Informatics and Decision Making 2024 24:365
Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predic...
Anhedonia and suicidal ideation are symptoms of major depressive disorder (MDD) that are not regularly captured in structured scales but may be captured in unstructured clinical notes. Natural language process...
In Cameroon, like in many other resource-limited countries, data generated by health settings including morbidity and mortality parameters are not always uniform. In the absence of a national guideline necessa...
This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).
Rich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be app...
The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. The objec...
Reference intervals (RIs) are crucial for distinguishing healthy from sick individuals and vary across age groups. Hemoglobinopathies are common in Pakistan, making the quantification of hemoglobin variants es...
Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detect...
Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold sta...
The objective of this study was to examine the causal relationship between the usage of patient portals and patients’ self-care self-efficacy and satisfaction in care outcomes in the context of cancer care.
The prevalence and chronic nature of Inflammatory Bowel Diseases (IBD) is a significant global concern. As the essential part of treatments approach, patient adherence to treatment protocols and self-managemen...
Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and aut...
Medical decision-making is a complex multi-stage process. Chinese cancer patients’ preference for participation in decision-making stages, family involvement and influencing factors remain unclear.
To construct a nomogram combining CT varices vein evaluation and clinical laboratory tests for predicting the risk of esophageal gastric variceal bleeding (EGVB) in patients with noncirrhotic portal hypertensi...
The practical application of infectious disease emergency plans in mental health institutions during the ongoing pandemic has revealed significant shortcomings. These manifest as chaotic management of mental h...
The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent year...
Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training coho...
The original article was published in BMC Medical Informatics and Decision Making 2020 20:111
Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy. Late diagnosis can have significant implications for both the mother and the fetus. This research aims to create an early predicti...
The original article was published in BMC Medical Informatics and Decision Making 2024 24:373
Digital health has emerged as a promising solution for enhancing health system in the recent years, showing significant potential in improving service outcomes, particularly in low and middle-income countries ...
Non-obstructive azoospermia (NOA), the severe type of male infertility. The objective of this study was to evaluate the predictive accuracy of a prediction model of sperm retrieval failure with fine needle asp...
Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose...
Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the ...
Automatic classification of arrhythmias based on electrocardiography (ECG) data faces several significant challenges, particularly due to the substantial volume of clinical data involved in ECG signal analysis...
Today, malnutrition is one of the biggest health crises for children in the world. Access to accurate and high-quality data is very important to establish policies to deal with it. Registries are considered va...
Citation Impact 2023
Journal Impact Factor: 3.3
5-year Journal Impact Factor: 3.9
Source Normalized Impact per Paper (SNIP): 1.304
SCImago Journal Rank (SJR): 1.002
Speed 2024
Submission to first editorial decision (median days): 9
Submission to acceptance (median days): 217
Usage 2024
Downloads: 3,248,174
Altmetric mentions: 1,208
The following summary describes the peer review process for this journal:
Identity transparency: Single anonymized
Reviewer interacts with: Editor
Review information published: Review reports. Reviewer Identities reviewer opt in. Author/reviewer communication