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Home » Basic and Health Sciences » Medicine » Health Science and Technology (M-I-T) » Collaborative Data Science for Healthcare (Fall 2020) (M-I-T) » Introduction (M-I-T)

Introduction (M-I-T)

Course:Collaborative Data Science for Healthcare (Fall 2020) (M-I-T)
Discipline:Basic and Health Sciences
Institute:MIT
Instructor(s): Dr. Leo Celi, Dr. Louis Agha-Mir-Salim, and Marie-Laure Charpignon
Level:Graduate

Collaborative Data Science for Healthcare (Fall 2020) (M-I-T)

A Data Science Revolution In Healthcare (M-I-T)
A Data-Driven Future For Medicine (M-I-T)
Adequate Representation and Justification of Data (M-I-T)
An example in R with MIMIC-III (M-I-T)
Best-Practices to Ensure Reproducibility (M-I-T)
Case Study: Reproducing Studies Using MIMIC-III (M-I-T)
Closing Remarks (M-I-T)
Intervention/Exposure (M-I-T)
Closing Summary and Next Steps for Independent Learning (M-I-T)
Introduction (M-I-T)
Collaborations and Data Source Bias (M-I-T)
Introduction to Regression Analysis: Part I (M-I-T)
Conclusion (M-I-T)
Introduction to Regression Analysis: Part II (M-I-T)
Considerations Of Fairness (M-I-T)
Iris Dataset and Decision Tree Introduction (M-I-T)
Control Group (M-I-T)
Linear Regression: Reinforcement Material (optional) (M-I-T)
Deep Learning: A Machine Learning Technique (M-I-T)
Logistic Regression: Contingency Tables (M-I-T)

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