A prognosis system for colorectal cancer

Abstract

The level of uncertainty and incompleteness in the information upon which healthcare professionals have to make judgments has been a subject of discussion in the past, and more nowadays, with the advent of the so-called Clinical Decision Support Systems. This work addresses uncertainty in the postoperative prognosis for colorectal cancer. The interdependence and synergistic effect of different clinical features comes into play when it is necessary to predict how a patient will react to this type of surgery. Using a probabilistic based knowledge representation, a decision support system was conceived in order to provide support for physicians under these circumstances, in particular to surgeons. The solution proposed is based on machine learning on records of cancer patients, incorporating explicit knowledge of experts about the domain. To facilitate access and thus increase its dissemination in the healthcare community, the system is integrated in a wider platform available through a web application.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

“…With low prediction error and good generalization performance, the two-stage model could help make treatment decisions, improve patient satisfaction, save medical resources, and reduce medical costs[ 141 ]. Based on the knowledge representation method of probability, Oliveira et al [ 142 ] designed a Clinical Decision Support System (CDSS) which, based on the cancer patients’ records and the precise knowledge of experts, could propose an effective treatment scheme and solve the uncertainty of prognosis after surgery[ 142 ]. CDSS could complete four basic tasks: Data organization, data collection, the combination of various principles and specific data, and user-friendly display of analysis results.…”

Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study

“…Several studies in the neonatal field have used supervised learning methods, for example, support vector machine (SVM), artificial neural network, decision tree, K-nearest neighbor (KNN), and random forests have been used in diagnosing and predicting neonatal diseases, such as jaundice (4)(5)(6)(7)(8), extubation failure for neonates with RDS (9)(10)(11), neonatal death (12), RDS and hypoglycemia, infant mortality (13), low birth weight (14)(15)(16)(17)(18), apnea (19), neonatal resuscitation, early postoperative survival in infant heart transplantation (20), metabolic disorder and prematurity (21).…”