论文标题
IMEDBOT：基于网络的智能代理，用于医疗保健相关的预测和深度学习
iMedBot: A Web-based Intelligent Agent for Healthcare Related Prediction and Deep Learning
论文作者
论文摘要
背景：乳腺癌是一种多因素疾病，遗传和环境因素将影响其发病率。乳腺癌转移是美国癌症协会（ACS）报道的乳腺癌相关死亡的主要原因之一。方法：IMEDBOT是我们使用Python Flask Web框架并在Amazon Web服务上部署的Web应用程序。它包含一个前端和后端。我们使用Python Keras和Scikit-Learn软件包开发的Python程序支持了后端，这些程序可用于学习深层馈送神经网络（DFNN）模型。结果：IMEDBOT可以提供两种主要服务：1。它可以根据用户提供的一组临床信息来预测5年，10或15年的乳腺癌转移。该预测是通过使用鉴定的DFNN型号和2的2。可以使用用户提供的数据集训练用户训练DFNN模型。将使用AUC评估经过训练的模型，并将提供AUC值和AUC ROC曲线。结论：IMEDBOT Web应用程序在进行个性化预测和模型培训时为用户代理交互提供了一个用户友好的接口。这是将深度学习研究结果转换为在线工具的最初尝试，该工具可能会朝着这一方向引起进一步的研究兴趣。关键词：深度学习，乳腺癌，网络应用，模型培训。
Background: Breast cancer is a multifactorial disease, genetic and environmental factors will affect its incidence probability. Breast cancer metastasis is one of the main cause of breast cancer related deaths reported by the American Cancer Society (ACS). Method: the iMedBot is a web application that we developed using the python Flask web framework and deployed on Amazon Web Services. It contains a frontend and a backend. The backend is supported by a python program we developed using the python Keras and scikit-learn packages, which can be used to learn deep feedforward neural network (DFNN) models. Result: the iMedBot can provide two main services: 1. it can predict 5-, 10-, or 15-year breast cancer metastasis based on a set of clinical information provided by a user. The prediction is done by using a set of DFNN models that were pretrained, and 2. It can train DFNN models for a user using user-provided dataset. The model trained will be evaluated using AUC and both the AUC value and the AUC ROC curve will be provided. Conclusion: The iMedBot web application provides a user-friendly interface for user-agent interaction in conducting personalized prediction and model training. It is an initial attempt to convert results of deep learning research into an online tool that may stir further research interests in this direction. Keywords: Deep learning, Breast Cancer, Web application, Model training.
