AIMS: This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
Sep 19, 2024
INTRODUCTION: An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomi...
Computer methods and programs in biomedicine
Sep 19, 2024
BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to ha...
International journal of medical informatics
Sep 19, 2024
INTRODUCTION: Data collection often relies on time-consuming manual inputs, with a vast amount of information embedded in unstructured texts such as patients' medical records and clinical notes. Our study aims to develop a pipeline that combines acti...
BACKGROUND: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence.
BACKGROUND: Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improv...
OBJECTIVE: This study aims to analyze the application and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes.
The Journal of thoracic and cardiovascular surgery
Sep 18, 2024
OBJECTIVE: The study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making.
RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.