Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death.
This study proposes a multi-modal deep neural network for multi-class malignant liver …

Depression is a challenge to public health, frequently related to disability and one of the
reasons that lead to suicide. Many of the ones who suffer depression use social media to …

Background At present, the diagnostic ability of hepatocellular carcinoma (HCC) based on
serum alpha-fetoprotein level is limited. Finding markers that can effectively distinguish …

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major
hurdle in its management. Appropriate staging and treatment selection may alleviate the …

Background Early detection of complex diseases like hepatocellular carcinoma remains
challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) …

Aiming at the problem of depression recognition, this paper proposes a computer-aided
recognition framework based on decision-level multimodal fusion. In Song Dynasty of China …

Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research.
Despite the high lethality, methods to predict mortality for this type of aggressive malignant …

Purpose Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart
rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional …