BMC medical informatics and decision making
May 27, 2024
Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, an...
BMC medical informatics and decision making
May 27, 2024
OBJECTIVE: Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction ...
BMC medical informatics and decision making
May 27, 2024
BACKGROUND: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict ass...
BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the e...
Early assessment and accurate staging of liver fibrosis may be of great help for clinical diagnosis and treatment in patients with chronic hepatitis B (CHB). We aimed to identify serum markers and construct a machine learning (ML) model to reliably p...
Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission inte...
Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate me...
BACKGROUND: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findi...
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the developme...
This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine le...
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