Medical decision making : an international journal of the Society for Medical Decision Making
Oct 23, 2024
BACKGROUND: Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop...
The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely u...
In today's world, services are essential in daily life, and identifying each person's unique needs is key to creating a human-centered society. Traditional research has used machine learning to recommend services based on user behavior logs without d...
BACKGROUND: Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofib...
This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient characteristics and single nucleotide polymorphisms of and . 2405 patients were analyzed to measure the M...
Driving anger is a serious global issue that poses risks to road safety, thus necessitating the development of effective detection and intervention methods. This study investigated the feasibility of using smartphones to capture facial expressions to...
Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a ri...
Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hindered by limited datasets. To address this limitation, we introduced a robust GNN training framework...
Journal of pediatric hematology/oncology
Oct 22, 2024
Childhood acute myeloid leukemia (AML) requires intensive chemotherapy, which may result in life-threatening bloodstream infections (BSIs). This study evaluated whether machine learning (ML) could predict BSI using electronic medical records. All chi...
Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points...
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