Machine Learning For Digital Healthcare

Machine Learning For Digital Healthcare

Day 1: Introduction

1. Morning Session: Welcome and Overview

   – Registration and welcome remarks

   – Workshop objectives and schedule overview

   – Importance of machine learning in healthcare

2. Morning Session: Fundamentals of Machine Learning

   – Introduction to machine learning concepts

   – Supervised, unsupervised, and reinforcement learning

   – Real-world healthcare applications of machine learning

3. Afternoon Session: Data in Healthcare

   – Challenges and opportunities in healthcare data

   – Data preprocessing and cleaning for healthcare datasets

   – Hands-on exercise: Data preprocessing with Python libraries

Day 2: Medical Imaging and Diagnostics

1. Morning Session: Medical Imaging and Deep Learning

   – Introduction to medical imaging and its importance

   – Using deep learning for image classification and segmentation

   – Case study: Detecting diseases from medical images

2. Afternoon Session: Hands-on Workshop – Medical Image Classification

   – Setting up a deep learning environment

   – Implementing a medical image classification model

   – Evaluating model performance with real medical images

Day 3: Predictive Modeling in Healthcare

1. Morning Session: Predictive Analytics in Healthcare

   – Predictive modeling for patient outcomes

   – Feature selection and engineering for healthcare datasets

   – Case study: Predicting disease risk with patient data

2. Afternoon Session: Hands-on Workshop – Predictive Modeling

   – Building a predictive model for healthcare data

   – Model evaluation and interpretation

   – Discussing ethical considerations in healthcare predictions

Day 4: Workshop Projects and Future Directions

1. Morning Session: Workshop Projects

   – Participants work on healthcare-related machine learning projects in groups

   – Mentors provide guidance and support

2. **Afternoon Session: Project Presentations and Discussion**

   – Each group presents their project

Day 5: Direction and Next steps

3. Closing Session: Future Directions and Resources

   – Discussion of the future of machine learning in healthcare

   – Resources for further learning and staying updated

   – Certificate distribution and closing remarks