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Polycystic Ovarian Disease or Polycystic Ovary Syndrome (PCOS) is becoming increasingly communal among women, owing to poor lifestyle choices. According to the research conducted by National Institutes of Heal...
Medical dispute is a global public health issue, which has been garnering increasing attention. In this study, we used machine learning (ML) method to establish a dispute prediction model and explored the clin...
Electronic prescribing (e-prescribing) is an essential technology in the modern health system. This technology has made many changes in the prescription process, which have advantages and disadvantages and hav...
Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. However, those whose symptoms significantly improve and remain stable do not need to conform to a regular follow-up sched...
Fibroids are non-cancerous uterine growths that can cause symptoms impacting quality of life. The breadth of treatment options allows for patient-centered preference. While conversation aids are known to facil...
Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. Therefore, priority should be given to the more important ones, ...
Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating a...
The International Classification of Diseases, 11th Revision (ICD-11) has significantly improved the ability to navigate coding challenges beyond prior iterations of the ICD. Commonly encountered sources of com...
This article is part of a Supplement: Volume 21 Supplement 6
In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases...
The Correction to this article has been published in BMC Medical Informatics and Decision Making 2024 24:308
Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured th...
Women with high-risk breast lesions, such as atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS), have a 4- to tenfold increased risk of breast cancer compared to women with non-proliferative breast ...
Cephalometric analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection o...
Early identification of frail patients and early interventional treatment can minimize the frailty-related medical burden. This study investigated the use of machine learning (ML) to detect frailty in hospital...
Parkinson’s disease (PD) is classified as a neurological, progressive illness brought on by cell death in the posterior midbrain. Early PD detection will assist doctors in reducing the disease’s consequences. ...
Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.
Interest in mental health smartphone applications has grown in recent years. Despite their effectiveness and advantages, special attention needs to be paid to two aspects to ensure app engagement: to include p...
Continuous renal replacement therapy (CRRT) is a life-saving procedure for sepsis but the benefit of CRRT varies and prediction of clinical outcomes is valuable in efficient treatment planning. This study aime...
Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybri...
The original article was published in BMC Medical Informatics and Decision Making 2024 24:249
Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and per...
Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of descri...
This article is part of a Supplement: Volume 24 Supplement 5
Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses...
Graded diagnosis and treatment, referral, and expert consultations between medical institutions all require cross domain access to patient medical information to support doctors’ treatment decisions, leading t...
The population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatme...
This article is part of a Supplement: Volume 24 Supplement 2
The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record syst...
Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to thi...
Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cau...
The aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate nea...
Electronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs)...
The association between red blood cell distribution width (RDW) to albumin ratio (RAR) and prognosis in patients with acute respiratory failure (ARF) admitted to the Intensive Care Unit (ICU) remains unclear. ...
This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addresses the challenges and innovative approaches in combating i...
To analyze primary angle closure suspect (PACS) patients’ anatomical characteristics of anterior chamber configuration, and to establish artificial intelligence (AI)-aided diagnostic system for PACS screening.
This study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions.
Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques...
The Correction to this article has been published in BMC Medical Informatics and Decision Making 2024 24:264
Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the ...
Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in...
The worldwide prevalence of type 2 diabetes mellitus in adults is experiencing a rapid increase. This study aimed to identify the factors affecting the survival of prediabetic patients using a comparison of th...
The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliabil...
Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this do...
Data quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems.
Modeling patient data, particularly electronic health records (EHR), is one of the major focuses of machine learning studies in healthcare, as these records provide clinicians with valuable information that ca...
Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discri...
The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data...
Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. Ho...
This article is part of a Supplement: Volume 23 Supplement 1
The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as...
Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful tr...
To investigate how successfully the classification of patients with and without dental anomalies was achieved through four experiments involving different dental anomalies.
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutti...
Systemic inflammatory response syndrome (SIRS) is a predictor of serious infectious complications, organ failure, and death in patients with severe polytrauma and is one of the reasons for delaying early total...
Responding to the rising global prevalence of noncommunicable diseases (NCDs) requires improvements in the management of high blood pressure. Therefore, this study aims to develop an explainable machine learni...
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