浏览：乳腺癌治疗的临床决策支持系统
CureMate: A clinical decision support system for breast cancer treatment
影响因子:4.10000
分区:医学2区 / 卫生保健与服务2区 计算机：信息系统3区 医学：信息3区
发表日期:2024 Dec
作者:
Rodrigo Martín Gómez Del Moral Herranz, María Jesús López Rodríguez, Alexander P Seiffert, Javier Soto Pérez-Olivares, Miguel Chiva De Agustín, Patricia Sánchez-González
摘要
乳腺癌（BC）在治疗决策中构成了重大挑战。目前可用多个第一治疗线，由在决策过程中需要考虑的几个特定于患者的因素确定。为了预测bc患者最有效的初始治疗的临床决策支持系统。研究了基于人口统计学，解剖学和磁共振成像变量的不同人工智能模型。 Curemate的Web应用程序可以轻松使用最佳型号。建立了232名公元前232名患者的数据库，每个数据库由29个变量描述。在四种机器学习算法中，特别是决策树分类器（DTC），高斯幼稚的贝叶斯（GNB），k-neartible邻居（K-NN）和支持向量机（SVM），最适合任务的模型已被确定，优化和独立测试。SVM被确定为BC处理的最佳模型。策展人的Web应用程序（包括SVM模型）允许引入相关的患者变量，并显示了建议的第一个治疗步骤，以及以下步骤的图表。结果表明，策展人在临床环境中的高精度和有效性表明其有助于实践者在做出知识治疗决策方面的潜力。
Abstract
Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process.To present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate's web application allows for easy use of the best model.A database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested.SVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate's web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps.The results demonstrate CureMate's high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions.