Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. Then, we look at key concepts such as study design, sample size and overfitting.
Gratuit
Accès gratuit à l'ensemble des cours, lectures et devoirs
Offert par
Population Health: Predictive Analytics
Université de LeydeÀ propos de ce cours
4,471 consultations récentes
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Basic familiarity with R, knowledge of basic statistical concepts. Completing 'Population Health: Responsible Data Analysis' beforehand recommended.
Approx. 17 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
Understand the role of predictive analytics for prevention, diagnosis, and effectiveness
Explain key concepts in prediction modelling: appropriate study design, adequate sample size and overfitting
Understand important issues in model development, such as missing data, non-linear relations and model selection
Know about ways to assess model quality through performance measures and validation
Compétences que vous acquerrez
Predictive Analyticsmedical statisticsR ProgrammingRegression Analysis
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Basic familiarity with R, knowledge of basic statistical concepts. Completing 'Population Health: Responsible Data Analysis' beforehand recommended.
Approx. 17 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université de Leyde
Leiden University is one of Europe's foremost research universities. This prominent position gives our graduates a leading edge and prepares them for careers both within and outside of academia. Leiden University is the oldest university in the Netherlands, founded in 1575. Our motto is: Praesidium Libertatis (Bastion of Liberty) - Freedom of spirit, thought and expression. Leiden University has a campus in Leiden and The Hague, with 7 faculties, 47 Bachelor Programmes, 79 Master Programmes and nearly 30,000 students.
Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
Welcome to Leiden University
Welcome to the course Predictive Analytics! We are excited to have you in class and look forward to your contributions to the learning community. To begin, we recommend taking a few minutes to explore the course site. Review the material we will cover each week, and preview the assignments you will need to complete in order to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center.
2 heures pour terminer
2 vidéos (Total 7 min), 6 lectures
6 lectures
Meet the instructors & the team6 min
Leiden University: Facts & Figures10 min
About this course10 min
Glossary10 min
Community Guidelines 5 min
What is your learning path?5 min
3 heures pour terminer
Prediction for prevention, diagnosis, and effectiveness
In this module, we discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. We begin with a brief introduction to predictive analytics, which we follow by differentiating between population-based and targeted interventions. We then explain why and when it may be beneficial to test for a diagnosis, and how analytic tools can help inform these decisions. Finally, we focus on the balance between benefits and harms of a certain treatment, and how we can predict the benefit for an individual.
3 heures pour terminer
6 vidéos (Total 28 min)
6 vidéos
Introduction1 min
Introduction to predictive analytics6 min
Predictive analytics in prevention4 min
Predictive analytics diagnosis7 min
Predictive analytics in intervention5 min
To conclude1 min
6 exercices pour s'entraîner
Introductory assignment30 min
Prevention assignment30 min
Diagnosis assignment20 min
Intervention assignment20 min
Reflect on your goals10 min
Test your knowledge20 min
4 heures pour terminer
Modeling Concepts
In this module, we will present some key concepts in prediction modeling. First, we weigh the strengths and weakness of various study designs. Second, we stress the importance of an appropriate sample size for reliable inference. Then, we discuss the issues of overfitting a prediction model, and regression-to-the-mean. Finally, we will guide you through the popular bootstrap procedure, showing how it can be used to assess parameter variability.
4 heures pour terminer
6 vidéos (Total 30 min), 3 lectures, 3 quiz
6 vidéos
Introduction1 min
Design issues5 min
Sample size7 min
Overfitting7 min
Bootstrapping6 min
To conclude1 min
3 lectures
Is caring about measurement error an error?20 min
Sample size30 min
Bootstrapping 101 in R30 min
3 exercices pour s'entraîner
Testimation bias - an interactive introduction1 h
Reflect on your goals10 min
Test your knowledge30 min
4 heures pour terminer
Model development
In this module, we focus on model development. First, we turn our attention to the missing values problem. We discuss well-known missingness mechanisms, and methods to deal with missing values appropriately. Second, we learn about methods to deal with non-linearity in a dataset. We then address the topic of model selection, focusing on the limitations of traditional stepwise selection procedures. Last, we talk about how introducing bias in exchange for lower variance can improve prediction quality. This can be done by using advanced methods, such as LASSO and Ridge regression.
4 heures pour terminer
6 vidéos (Total 30 min), 4 lectures, 2 quiz
6 vidéos
Introduction51s
Missing values7 min
Continuous predictors8 min
Model selection6 min
Model estimation5 min
To conclude1 min
4 lectures
Bias, precision and simple imputation of missing values40 min
Dealing with non-linearity30 min
Model selection25 min
Model estimation30 min
2 exercices pour s'entraîner
Reflect on your goals10 min
Test your knowledge
5 heures pour terminer
Model validation and updating
In this final module, we learn about assessing the quality of a prediction model. First, we extensively discuss standard performance measures for both binary and continuous outcomes. Second, we explore different ways of validating a prediction model. We look at how to assess both the internal, and the more relevant external validity of a model. Next, we will look at how to update a model and make it applicable to a specific medical setting. We conclude with an interview, where we more broadly discuss the potential of predictive analytics by taking the example of the island of Aruba.
5 heures pour terminer
6 vidéos (Total 33 min), 2 lectures, 5 quiz
6 vidéos
Introduction48s
Performance measures6 min
Validation approaches7 min
Updating approaches5 min
Predictive analytics for Aruba11 min
To conclude1 min
2 lectures
Performance I - Statistical measures1 h
Performance II - Evaluation of usefulness1 h
5 exercices pour s'entraîner
Recall - Performance I15 min
Validation cardiovascular disease30 min
Reflect on your goals10 min
Test your knowledge20 min
Final Assessment1 h
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
À quoi ai-je droit si j'achète le Certificat ?
Lorsque vous achetez un Certificat, vous bénéficiez d'un accès à tout le contenu du cours, y compris les devoirs notés. Lorsque vous avez terminé et réussi le cours, votre Certificat électronique est ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
What is the refund policy?
Vous avez droit à un remboursement intégral jusqu'à deux semaines après la date de paiement ou (pour les cours qui viennent d'être lancés) jusqu'à deux semaines après le début de la première session du cours, selon la dernière de ces éventualités. Vous ne pouvez pas bénéficier d'un remboursement une fois votre Certificat de Cours obtenu, même si vous terminez le cours pendant la période de remboursement de deux semaines. Consultez notre Politique de remboursement complète.
Is financial aid available?
Oui, Coursera fournit une Aide Financière aux étudiants n'ayant pas les moyens d'acquitter les frais. Pour en faire la demande, cliquez sur le lien Aide Financière situé sous le bouton S'inscrire ci-contre à gauche. Vous serez invité(e) à déposer une demande et vous serez averti(e) si elle est acceptée. En savoir plus.
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
