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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai-for-medical-prognosis
课程评论：没有评论
课程名称：医疗预后中的人工智能 概述：人工智能正在改变医学实践。它帮助医生更准确地诊断患者，预测患者未来的健康状况，并推荐更好的治疗方案。本课程专注于提供实践经验，将机器学习应用于医学中的具体问题。机器学习是预后研究的重要工具，专注于预测患者的未来健康情况。在本课程中，您将提供多种预后任务的实例，然后使用决策树来建模非线性关系，这种关系在医学数据中特别常见，并将其应用于更准确的死亡率预测。最后，您将学习如何处理缺失数据，这是现实世界中的一大挑战。 本课程不仅涵盖深度学习的基础知识，还教授将人工智能应用于医疗案例中的细微差别。课程侧重于基于树的机器学习，因此不要求具备深度学习的基础。然而，对于本专业化的第1和第3课程，建议具备深度学习的基础。您可以通过参加由deeplearning.ai提供、Andrew Ng教授的深度学习专业课程来获得深度学习的基础知识。 课程大纲： 第1部分：线性预后模型 描述：构建一个线性预后模型，使用逻辑回归，然后通过计算一致性指数来评估模型。最后，通过添加特征交互来改进模型。 第2部分：基于树的模型的预后 描述：调整决策树和随机森林模型，以预测疾病风险。使用c-index评估模型性能。识别缺失数据及其如何影响数据分布，并利用插补填补缺失数据，以提高模型性能。 第3部分：生存模型与时间 描述：本周，您将处理时间作为变量的数据，构建更灵活的模型，预测5年、7年或10年的疾病风险，而不仅仅是预测10年风险。 第4部分：使用线性和基于树的模型构建风险模型 描述：本周，您将对生存数据拟合线性模型和基于树的风险模型，为每位患者定制风险评分，基于他们的健康状况生成相对疾病风险的评分。然后，您将通过实施并使用结合事件时间和删失数据的一致性指数来评估每个模型的性能。
Part: 1
Title:Linear Prognostic Models
Description:Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.
Part: 2
Title:Prognosis with Tree-based Models
Description:Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.
Part: 3
Title:Survival Models and Time
Description:This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.
Part: 4
Title:Build a Risk Model Using Linear and Tree-based Models
Description:This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.
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. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. This course focuses on tree-based machine learning, so a foundation in deep learning is not required for this course. However, a foundation in deep learning is highly recommended for course 1 and 3 of this specialization. You can gain a foundation in deep learning by taking the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.
