AI Medical Compendium Journal:
BMC medical informatics and decision making

Showing 111 to 120 of 718 articles

Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.

BMC medical informatics and decision making
BACKGROUND: 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.  METHODS: In this study, di...

Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: 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).

Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.

BMC medical informatics and decision making
BACKGROUND: 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 processing (NLP) techniques may be u...

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.

BMC medical informatics and decision making
BACKGROUND: 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 standard for UTI diagnosis is ur...

A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.

BMC medical informatics and decision making
BACKGROUND: 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 years, advancements in the analys...

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: 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 cohort and perform worse at new h...

Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.

BMC medical informatics and decision making
BACKGROUND: 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 prediction model for GDM in the first...

A new risk assessment model of venous thromboembolism by considering fuzzy population.

BMC medical informatics and decision making
BACKGROUND: 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 impact of class-imbalance pro...

Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis.

BMC medical informatics and decision making
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 of this research is to offer novel deep-...

The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data.

BMC medical informatics and decision making
BACKGROUND: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health ...