Titlebook: Leveraging Data Science for Global Health; Leo Anthony Celi,Maimuna S. Majumder,Melek Somai Textbook‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applic

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Machine Learning for Clinical Predictive Analyticsliability. In the second section, we will introduce several important concepts in machine learning in a colloquial manner, such as learning scenarios, objective/target function, error and loss function and metrics, optimization and model validation, and finally a summary of model selection methods (

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itional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient..978-3-030-47996-1978-3-030-47994-7

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https://doi.org/10.1007/978-3-030-47994-7Open Access; Big Data; Machine Learning; Artificial Intelligence; Health Informatics; Digital Disease Sur

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Machine Learning for Patient Stratification and Classification Part 1: Data Preparation and Analysisugh the basic concepts underlying machine learning and the tools needed to easily implement it using the Python programming language and Jupyter notebook documents. It is divided into three main parts: part 1—data preparation and analysis; part 2—unsupervised learning for clustering, and part 3—supervised learning for classification.

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Machine Learning for Patient Stratification and Classification Part 2: Unsupervised Learning with Clugh the basic concepts underlying machine learning and the tools needed to easily implement it using the Python programming language and Jupyter notebook documents. It is divided into three main parts: part 1—data preparation and analysis; part 2—unsupervised learning for clustering and part 3—supervised learning for classification.