In the 21st century, the development of medical science has entered the era of big data, and
machine learning has become an essential tool for mining medical big data. The …

Osteoporosis is a major public health concern that significantly increases the risk of
fractures. The aim of this study was to develop a Machine Learning based predictive model …

Background The use of machine learning has the potential to estimate the probability of a
second classification event more accurately than traditional statistical methods, and few …

Streamlit is an open-source Python coding framework for building web-applications or “web-
apps” and is now being used by researchers to share large data sets from published studies …

Current medical limitations in predicting cancer survival status and time necessitate
advancements beyond traditional methods and physical indicators. This research introduces …

Background The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is
essential for determining the scope of surgical resection. It also affects the long-term efficacy …

Background Identifying patients with hepatocellular carcinoma (HCC) at high risk of
recurrence after hepatectomy can help to implement timely interventional treatment. This …

Purpose The aim of this study was to established a dynamic nomogram for assessing the
risk of bone metastasis in patients with thyroid cancer (TC) and assist physicians to make …

In recent years, we have witnessed an exponential growth in the generation of data in the
biological sciences. To harness such big biological data, computational and machine …

Objective: Distant metastasis other than non-regional lymph nodes and lung (ie, M1b stage)
significantly contributes to the poor survival prognosis of patients with germ cell testicular …