Thoracic surgery involves the surgical treatment of vital organs inside the thoracic cavity to
treat conditions of the lungs, heart, trachea, diaphragm, etc. Such procedures are primarily …

Thoracic surgery refers to the information gathered for the patients who have to suffer from
lung cancer. Various machine learning techniques were employed in post-operative life …

The scope of this paper is to propose a life expectancy rate and examine the mortality after
thoracic surgery which takes into account the different importance of various features which …

The nature of clinical data makes it difficult to quickly select, tune and apply machine
learning algorithms to clinical prognosis. As a result, a lot of time is spent searching for the …

Background We developed machine learning models that combine preoperative and
intraoperative risk factors to predict mortality after cardiac surgery. Methods Machine …

Background Machine learning (ML) algorithms may enhance outcomes prediction and help
guide clinical decision making. This study aimed to develop and validate a ML model that …

Cardiac patients undergoing surgery face increased risk of postoperative complications, due
to a combination of factors, including higher risk surgery, their age at time of surgery and the …

Objective: This study aims to construct and validate several machine learning (ML)
algorithms to predict long-term mortality and identify risk factors in unselected patients post …

Background The objective of this study was to predict the mortality risk of patients during or
shortly after cardiac surgery by using machine learning techniques and their learning …

OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has
bloomed recently. Cardiac surgery patients are at high risk of complications and therefore …