Articles
Page 1 of 66
-
Citation: BMC Medical Informatics and Decision Making 2024 24:89
-
A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests
The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-fr...
Citation: BMC Medical Informatics and Decision Making 2024 24:88 -
Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset
Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in...
Citation: BMC Medical Informatics and Decision Making 2024 24:86 -
Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma
Patients with renal cell carcinoma (RCC) have an elevated risk of chronic kidney disease (CKD) following nephrectomy. Therefore, continuous monitoring and subsequent interventions are necessary. It is recommen...
Citation: BMC Medical Informatics and Decision Making 2024 24:85 -
Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis
Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in I...
Citation: BMC Medical Informatics and Decision Making 2024 24:84 -
An Effective Methodology for Scoring to Assist Emergency Physicians in Identifying Overcrowding in an Academic Emergency Department in Thailand
Emergency Department (ED) overcrowding is a global concern, with tools like NEDOCS, READI, and Work Score used as predictors. These tools aid healthcare professionals in identifying overcrowding and preventing...
Citation: BMC Medical Informatics and Decision Making 2024 24:83 -
Multimodal deep learning-based diagnostic model for BPPV
Benign paroxysmal positional vertigo (BPPV) is a prevalent form of vertigo that necessitates a skilled physician to diagnose by observing the nystagmus and vertigo resulting from specific changes in the patien...
Citation: BMC Medical Informatics and Decision Making 2024 24:82 -
Development of a communication platform for patients with head and neck cancer for effective information delivery and improvement of doctor–patient relationship: application of treatment journey-based service blueprint
Effective communication and information delivery enhance doctor–patient relationships, improves adherence to treatment, reduces work burden, and supports decision-making. The study developed a head and neck ca...
Citation: BMC Medical Informatics and Decision Making 2024 24:81 -
Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis
Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and...
Citation: BMC Medical Informatics and Decision Making 2024 24:80 -
Development of a new computer simulated environment to screen cognition: assessing the feasibility and acceptability of Leaf Café in younger and older adults
Existing traditional cognitive screening tools for dementia have various limitations, including overreliance on tests assessing verbal memory and, to a lesser extent, on some aspects of executive functioning. ...
Citation: BMC Medical Informatics and Decision Making 2024 24:79 -
Communicating the results of risk-based breast cancer screening through visualizations of risk: a participatory design approach
Risk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women’s beliefs and knowledge (i.e., mental models) rega...
Citation: BMC Medical Informatics and Decision Making 2024 24:78 -
An automated ICU agitation monitoring system for video streaming using deep learning classification
To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intellige...
Citation: BMC Medical Informatics and Decision Making 2024 24:77 -
SurvInt: a simple tool to obtain precise parametric survival extrapolations
Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid ...
Citation: BMC Medical Informatics and Decision Making 2024 24:76 -
Exploring the potential of ChatGPT in medical dialogue summarization: a study on consistency with human preferences
Telemedicine has experienced rapid growth in recent years, aiming to enhance medical efficiency and reduce the workload of healthcare professionals. During the COVID-19 pandemic in 2019, it became especially c...
Citation: BMC Medical Informatics and Decision Making 2024 24:75 -
Deep learning of movement behavior profiles and their association with markers of cardiometabolic health
Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activit...
Citation: BMC Medical Informatics and Decision Making 2024 24:74 -
KGSCS—a smart care system for elderly with geriatric chronic diseases: a knowledge graph approach
The increasing aging population has led to a shortage of geriatric chronic disease caregiver, resulting in inadequate care for elderly people. In this global context, many older people rely on nonprofessional ...
Citation: BMC Medical Informatics and Decision Making 2024 24:73 -
Assessing the research landscape and clinical utility of large language models: a scoping review
Large language models (LLMs) like OpenAI’s ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in ...
Citation: BMC Medical Informatics and Decision Making 2024 24:72 -
Implementation difficulties and solutions for a smart-clothes assisted home nursing care program for older adults with dementia or recovering from hip fracture
Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. Smart clothing can provide feedback to family caregivers of older adults with disabilities who ...
Citation: BMC Medical Informatics and Decision Making 2024 24:71 -
Development and validation of ‘Patient Optimizer’ (POP) algorithms for predicting surgical risk with machine learning
Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate ...
Citation: BMC Medical Informatics and Decision Making 2024 24:70 -
Developing an integrated clinical decision support system for the early identification and management of kidney disease—building cross-sectoral partnerships
The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is...
Citation: BMC Medical Informatics and Decision Making 2024 24:69 -
A framework for inferring and analyzing pharmacotherapy treatment patterns
To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data.
Citation: BMC Medical Informatics and Decision Making 2024 24:68 -
Continual learning framework for a multicenter study with an application to electrocardiogram
Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint r...
Citation: BMC Medical Informatics and Decision Making 2024 24:67 -
Feasibility of a wearable self-management application for patients with COPD at home: a pilot study
Among people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to...
Citation: BMC Medical Informatics and Decision Making 2024 24:66 -
MMIR: an open-source software for the registration of multimodal histological images
Multimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from variou...
Citation: BMC Medical Informatics and Decision Making 2024 24:65 -
Identification of health-related problems in youth: a mixed methods feasibility study evaluating the Youth Health Report System
Because poor health in youth risk affecting their entry in adulthood, improved methods for their early identification are needed. Health and welfare technology is widely accepted by youth populations, presenti...
Citation: BMC Medical Informatics and Decision Making 2024 24:64 -
A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all
Adults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of q...
Citation: BMC Medical Informatics and Decision Making 2024 24:63 -
Seasonally adjusted laboratory reference intervals to improve the performance of machine learning models for classification of cardiovascular diseases
Variation in laboratory healthcare data due to seasonal changes is a widely accepted phenomenon. Seasonal variation is generally not systematically accounted for in healthcare settings. This study applies a ne...
Citation: BMC Medical Informatics and Decision Making 2024 24:62 -
Natural language processing to identify lupus nephritis phenotype in electronic health records
Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifest...
Citation: BMC Medical Informatics and Decision Making 2024 22(Suppl 2):348 -
A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure
Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. Thi...
Citation: BMC Medical Informatics and Decision Making 2024 24:60 -
Effect of early serum phosphate disorder on in-hospital and 28-day mortality in sepsis patients: a retrospective study based on MIMIC-IV database
This study aims to assess the influence of early serum phosphate fluctuation on the short-term prognosis of sepsis patients.
Citation: BMC Medical Informatics and Decision Making 2024 24:59 -
Conceptual design of a generic data harmonization process for OMOP common data model
To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is ...
Citation: BMC Medical Informatics and Decision Making 2024 24:58 -
A novel estimator for the two-way partial AUC
The two-way partial AUC has been recently proposed as a way to directly quantify partial area under the ROC curve with simultaneous restrictions on the sensitivity and specificity ranges of diagnostic tests or...
Citation: BMC Medical Informatics and Decision Making 2024 24:57 -
Correction: Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic
Citation: BMC Medical Informatics and Decision Making 2024 24:56 -
Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings
This study aimed to assess the performance of OpenAI’s ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings.
Citation: BMC Medical Informatics and Decision Making 2024 24:55 -
Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models
Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is ther...
Citation: BMC Medical Informatics and Decision Making 2024 24:54 -
Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data
Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes an...
Citation: BMC Medical Informatics and Decision Making 2024 24:53 -
Which risk factor best predicts coronary artery disease using artificial neural network method?
Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD.
Citation: BMC Medical Informatics and Decision Making 2024 24:52 -
Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare
Diagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates of diagn...
Citation: BMC Medical Informatics and Decision Making 2024 24:51 -
InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography
This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (...
Citation: BMC Medical Informatics and Decision Making 2024 24:50 -
Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images
Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dim...
Citation: BMC Medical Informatics and Decision Making 2024 24:49 -
Systematic design of health monitoring systems centered on older adults and ADLs
Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. T...
Citation: BMC Medical Informatics and Decision Making 2024 23(Suppl 3):300 -
Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency
Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients suscep...
Citation: BMC Medical Informatics and Decision Making 2024 24:48 -
Data resource profile of an online database system for forensic mental health services
This paper introduces a forensic psychiatry database established in Japan and discusses its significance and future issues. The purpose of this Database, created under the Medical Treatment and Supervision Act...
Citation: BMC Medical Informatics and Decision Making 2024 24:47 -
Varying (preferred) levels of involvement in treatment decision-making in the intensive care unit before and during the COVID-19 pandemic: a mixed-methods study among relatives
In the intensive care unit (ICU) relatives play a crucial role as surrogate decision-makers, since most patients cannot communicate due to their illness and treatment. Their level of involvement in decision-ma...
Citation: BMC Medical Informatics and Decision Making 2024 24:46 -
DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients
Chronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring...
Citation: BMC Medical Informatics and Decision Making 2024 24:45 -
Usability and feasibility analysis of an mHealth-tool for supporting physical activity in people with heart failure
Physical inactivity and a sedentary lifestyle are common among people with heart failure (HF), which may lead to worse prognosis. On an already existing mHealth platform, we developed a novel tool called the A...
Citation: BMC Medical Informatics and Decision Making 2024 24:44 -
Prediction of Sjögren’s disease diagnosis using matched electronic dental-health record data
Sjögren’s disease (SD) is an autoimmune disease that is difficult to diagnose early due to its wide spectrum of clinical symptoms and overlap with other autoimmune diseases. SD potentially presents through ear...
Citation: BMC Medical Informatics and Decision Making 2024 24:43 -
Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques
The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent ...
Citation: BMC Medical Informatics and Decision Making 2024 24:42 -
Choice of refractive surgery types for myopia assisted by machine learning based on doctors’ surgical selection data
In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to ...
Citation: BMC Medical Informatics and Decision Making 2024 24:41 -
Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment
Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake ...
Citation: BMC Medical Informatics and Decision Making 2024 24:40
Annual Journal Metrics
-
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
2,443 Altmetric mentions
Peer-review Terminology
-
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