📄️ 9.1 Bagging and Boosting in Clinical Models
This section provides an introduction to bagging and boosting techniques in healthcare machine learning. It covers bagging, Random Forest, boosting, AdaBoost, gradient boosting, XGBoost, and LightGBM. A case study demonstrates their application in disease risk prediction. The section emphasizes the significance of ensemble techniques in clinical decision support systems and outlines challenges and considerations. The conclusion highlights the role of bagging and boosting in enhancing model accuracy and robustness for improved patient outcomes.
📄️ 9.2 Random Forests for Patient Outcomes
This section introduces Random Forests as a versatile algorithm for patient outcomes prediction. It explains how Random Forest works, its advantages, and its applications in healthcare. The case study focuses on predicting patient mortality, showcasing the algorithm's capabilities. The section also covers interpreting Random Forest results, challenges, and considerations. It concludes by highlighting Random Forests' significance in enhancing patient care through accurate outcome predictions.
📄️ 9.3 Gradient Boosted Trees in Disease Prognosis
This section introduces Gradient Boosted Trees (GBTs) as an advanced algorithm for disease prognosis and outcome prediction in healthcare. It explains the working principle of GBTs, their advantages, and their applications in healthcare. The case study demonstrates how GBTs can be used to predict the prognosis of heart disease patients. The section also covers interpreting GBT results, challenges, and considerations. It concludes by highlighting GBTs' significance in improving disease prognosis and treatment decision-making.
📄️ Resources
- Scikit-learn Ensemble Methods for Health Informatics