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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai-for-medical-treatment
课程评论：没有评论
课程名称：医疗治疗中的人工智能 概述：人工智能正在改变医学实践，帮助医生更准确地诊断患者、预测患者的未来健康状况并推荐更优的治疗方案。本课程将为您提供将机器学习应用于医疗实际问题的实践经验。医疗治疗可能会根据患者的现有健康状况产生不同的影响。在本课程的第三部分中，您将根据随机对照试验的数据，推荐更适合个体患者的治疗方案。在第二周，您将应用机器学习解释方法来解释复杂机器学习模型的决策过程。最后，您将使用自然语言实体提取和问答方法，自动化标记医学数据集的任务。 这些课程超越了深度学习的基础，教您将人工智能应用于医疗案例的细微差别。如果您是深度学习的新手或想深入了解神经网络的工作原理，建议您先学习深度学习专业化课程。 课程大纲： 第一部分：治疗效果估计 描述：在本周，您将学习如何分析随机对照试验的数据，解释多变量模型，评估治疗效果模型，以及解释用于治疗效果估计的机器学习模型。 第二部分：医学问答 描述：在本周，您将学习如何从临床报告中提取疾病标签，以及使用BERT进行问答。 第三部分：机器学习解释 描述：在本周，您将学习如何解释深度学习模型，以及机器学习中的特征重要性。
Part: 1
Title:Treatment Effect Estimation
Description:In this week, you will learn: How to analyze data from a randomized control trial, interpreting multivariate models, evaluating treatment effect models, and interpreting ML models for treatment effect estimation.
Part: 2
Title:Medical Question Answering
Description:In this week, you will learn how to extract disease labels from clinical reports, and also question answering with BERT.
Part: 3
Title:ML Interpretation
Description:In this week, you will learn how to interpret deep learning models, and also feature importance in machine learning.
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.
