Machine Learning Best Practices in Healthcare

This five-day intensive training will equip participants with practical knowledge and hands-on experience in statistical analysis, data preprocessing, and machine learning techniques tailored to medical datasets.

Participants will explore supervised and unsupervised learning approaches for extracting insights, predicting outcomes, and supporting medical decision-making. The training bridges theoretical understanding with real-world healthcare applications, leveraging clinical data to uncover patterns, assess risks, and enhance predictive accuracy in diagnosis and treatment planning.

Workshop Overview

Learning Outcomes

• Apply foundational statistical methods in healthcare.

• Perform exploratory data analysis on medical datasets.

• Clean, preprocess, and visualize clinical data.

• Build and evaluate machine learning models for classification and regression.

• Use supervised learning to predict medical outcomes.

• Employ unsupervised learning for clustering and anomaly detection.

• Communicate data-driven insights in medical contexts.

• Integrate domain knowledge to improve model accuracy.

• Utilize Python and libraries such as pandas and scikit-learn.

• Mastering related AI prompt engineering.

Detailed Course Schedule

• Day 1:

  • Data Exploration, visualization, and statistical KPIs

• Day 2:

  • Data Analysis

• Day 3:

  • Supervised Learning for Predictive Models

• Day 4:

  • Unsupervised Learning for data exploration

• Day 5:

  • Classic vs AI Tools with prompting best practices

• Exploratory data analysis (EDA) of patient datasets

• Data cleaning and handling missing data

• Supervised learning: logistic regression, decision trees, SVMs

• Unsupervised learning: k-means clustering, PCA

• Feature engineering for medical models

• Evaluation metrics: accuracy, precision, recall, ROC curves

• Use cases: disease prediction, risk stratification

• Live coding and prompt engineering solutions

What will it be about?