10-Minute Tutorial: Patient-Level Prediction or "PLP" (Jenna Reps)

Summary

The video script introduces the 'Odyssey' GitHub repository for the Patient Prediction package, a tool for developing and validating predictive models in healthcare. It covers the basics of prediction, focusing on disease onset, progression, and treatment response. The script offers a demo using the 'nomia' package, showcasing how to design models, select features, and perform internal validation. It also highlights customization options and a new Shiny app for model result visualization.

Takeaways

• 📚 The script introduces the 'Odyssey' GitHub repository, which is a patient prediction package for developing and validating prediction models.
• 🔮 It explains the purpose of the package, which is to predict the risk of future outcomes based on current data, focusing on modern prognostic models and diagnostic models.
• 🏥 The 'index' concept is discussed, which is a point in time such as a doctor's visit, the start of pregnancy, or the end of pregnancy, where predictions can be made about future health outcomes.
• 📈 The script highlights the ability to learn patterns from medical records to predict future risks, such as the likelihood of a stroke within a certain timeframe.
• 🚫 It clarifies that the package is not for causal inference but for personalizing risk predictions for future or current outcomes.
• 🎯 The main categories for prediction include disease onset, progression, treatment choice, treatment response, safety, and treatment adherence.
• 🛠️ The process of creating a model design is outlined, which involves specifying the target population, outcome, and time span for predictions.
• 🔧 The script discusses the customization options available for feature engineering, sampling, pre-processing, and model fitting within the package.
• 📊 The importance of data splitting for model development and performance estimation is emphasized, with options for customizing the data split process.
• 💾 The demonstration includes using the 'nomia' package, which contains data for testing and running the patient prediction package without needing external data.
• 📱 The script concludes with an overview of a new shiny app interface for viewing model results, diagnostics, and performance metrics like the ROC and calibration plots.

Q & A

What is the main purpose of the 'patient prediction' package discussed in the script?

The 'patient prediction' package is designed for developing and validating prediction models that can estimate the risk of future outcomes based on baseline features extracted from medical records.

What types of prediction models does the package support?

The package supports both classification and survival models, focusing on personalized risk prediction for outcomes such as disease onset, progression, treatment choice, treatment response, and safety.

What is the significance of the 'index' in the context of the script?

The 'index' refers to a specific point in time, such as a patient's visit to a doctor or the start of a condition, from which baseline features are considered for predicting future outcomes.

How does the package handle the prediction of disease onset or progression?

For disease onset or progression, the package considers the target population, the outcome to be predicted, and the time at risk. It uses data from medical records up to the index to predict the risk of a future event within a specified time frame.

What customization options does the package offer for model development?

The package allows for customization in various stages, including feature engineering, sampling, pre-processing, model fitting, and data splitting for internal validation. Users can write custom code to tailor these processes to their specific needs.

What is the role of the 'model design' in the package?

The 'model design' specifies the components necessary for prediction, including the target population, the outcome, and the time at risk. It guides the package in creating a prediction model tailored to the user's specific requirements.

How does the package handle feature extraction from medical records?

Users can specify what features or covariates they want to extract from the data, such as demographics, gender, age groups, recent drug usage, and conditions. The package also supports the use of vocabulary hierarchies for grouping these features.

What is the 'nomia' package mentioned in the script, and how is it used?

The 'nomia' package is a dataset provided for testing and demonstration purposes. It allows users to run the 'patient prediction' package without their own data, helping them understand how the package functions.

What is the purpose of the shiny app interface mentioned in the script?

The shiny app interface is a new feature that provides an interactive way to view and analyze the results of the prediction models. It allows users to explore model diagnostics, performance summaries, and various plots for a deeper understanding of the model outcomes.

How does the package handle the storage and retrieval of model results?

The package uses an SQLite database to store all the models and their results. This allows for easy retrieval and comparison of different models and their performances.

What kind of diagnostics does the package provide to assess the model design and data?

The package provides diagnostics to check for any issues in the model design and the data being used. This helps ensure that the models are developed correctly and that the data is suitable for the intended predictions.