Advanced Prognostic Predictive Modelling in Healthcare Data Analytics (Lecture
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Prognostic predictive modelling is an important aspect of healthcare data analytics, as it allows healthcare professionals to predict future outcomes for patients based on past data. In this lecture, we will delve into advanced techniques and methods used in prognostic predictive modelling to improve patient care and outcomes.
Topics that will be covered in this lecture include:
1. Machine learning algorithms for prognostic predictive modelling: We will explore various machine learning algorithms such as logistic regression, random forest, and support vector machines that are commonly used in healthcare data analytics to predict patient outcomes.
2. Feature selection and engineering: We will discuss the importance of feature selection and engineering in prognostic predictive modelling, and how these techniques can help improve the accuracy and reliability of predictions.
3. Model evaluation and validation: We will cover different methods for evaluating and validating prognostic predictive models, such as cross-validation and receiver operating characteristic (ROC) curve analysis, to ensure the models are robust and reliable.
4. Clinical applications of prognostic predictive modelling: We will explore real-world examples of how prognostic predictive modelling is used in clinical settings to predict outcomes for patients with various medical conditions, such as cancer, heart disease, and diabetes.
Overall, this lecture aims to provide healthcare professionals with a comprehensive understanding of advanced prognostic predictive modelling techniques in healthcare data analytics, and how they can be applied to improve patient care and outcomes. Join us to learn more about this important topic in healthcare data analytics!
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