Population Health: Predictive Analytics
Master predictive analytics for healthcare, covering model development, validation, and application in prevention, diagnosis, and treatment effectiveness.
Master predictive analytics for healthcare, covering model development, validation, and application in prevention, diagnosis, and treatment effectiveness.
This course explores the application of predictive analytics in population health management. Students will learn to develop, validate, and apply prediction models for prevention, diagnosis, and treatment effectiveness. The curriculum covers key concepts in prediction modeling, including study design, sample size considerations, and overfitting. Learners will gain practical skills in handling missing data, non-linear relationships, and model selection. The course emphasizes model validation, performance measures, and updating techniques. Using R, students will apply these concepts to real-world healthcare scenarios.
4.6
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What you'll learn
Understand the role of predictive analytics in prevention, diagnosis, and treatment effectiveness
Apply appropriate study designs and sample size calculations for prediction modeling
Develop strategies for handling missing data and non-linear relationships in health datasets
Implement model selection techniques and understand the bias-variance tradeoff
Assess model quality using various performance measures and validation approaches
Learn to update prediction models for specific healthcare settings
Skills you'll gain
This course includes:
1.96 Hours PreRecorded video
17 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course provides a comprehensive exploration of predictive analytics in population health management. Students will learn to develop and validate prediction models for prevention, diagnosis, and treatment effectiveness. The curriculum covers key concepts such as study design, sample size considerations, and overfitting. Learners will gain practical skills in handling missing data, modeling non-linear relationships, and performing model selection. The course emphasizes model validation techniques, performance measures, and methods for updating models to specific healthcare settings. Using R programming, students will apply these concepts to real-world healthcare scenarios, developing a deep understanding of how predictive analytics can improve decision-making in population health management.
Welcome to Leiden University
Module 1 · 45 Minutes to complete
Prediction for prevention, diagnosis, and effectiveness
Module 2 · 5 Hours to complete
Modeling Concepts
Module 3 · 3 Hours to complete
Model development
Module 4 · 3 Hours to complete
Model validation and updating
Module 5 · 8 Hours to complete
Fee Structure
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Instructors
Advancing Precision Medicine at Erasmus MC
David van Klaveren is an assistant professor in the Department of Public Health at Erasmus MC in Rotterdam. His career ambition is to empower Precision Medicine—tailoring medical treatment to the individual characteristics of each patient, resulting in better health outcomes and cost savings. To achieve this, he improves the derivation and validation of prediction models that guide individualized decisions on treatment and prevention. With a strong mathematical background, he has developed and will continue to develop new guidance on modeling techniques, model performance measures, and data usage. He received the Young Investigator Award from the Journal of Clinical Epidemiology in 2015 and earned a cum laude PhD degree from Erasmus MC in 2017. Before returning to Erasmus MC, he was an assistant professor in the Biomedical Data Sciences department at LUMC. Since 2015, he has been a visiting scholar at the Predictive Analytics and Comparative Effectiveness Center of Tufts Medical Center in Boston. He has served as an associate editor for BMC Medical Informatics and Decision Making and is currently an associate editor for BMC Diagnostic and Prognostic Research.
Leader in Prediction Modeling for Medical Decision-Making
Ewout W. Steyerberg is a renowned Professor of Clinical Biostatistics and Medical Decision Making at Leiden University Medical Center, where he has been chairing the Department of Biomedical Data Sciences since 2017. His academic journey began at Leiden University, where he studied medicine and later earned an MSc in Biomedical Sciences. He completed his PhD at Erasmus MC with a thesis focused on prognostic modeling for clinical decision-making. With over 25 years of experience, including a significant tenure at Erasmus MC, Steyerberg has dedicated his career to developing, validating, and updating prediction models that enhance medical decision-making. His research encompasses advanced statistical methods, including regression analysis and machine learning, applied across various medical domains. He is widely published, with over 1,000 peer-reviewed articles to his name, and is recognized for his influential textbook, Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, which serves as a key resource in the field. Steyerberg's contributions have earned him numerous accolades, including membership in the Royal Netherlands Academy of Arts and Sciences and the European Academy of Sciences and Arts, reflecting his pivotal role in advancing healthcare through statistical science.
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4.6 course rating
23 ratings
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