The Challenge of Prediction

• Prediction is a fundamental part of data science and analytics.
• Using what happened in the past can help inform us about the future.
• But data, on its own, can only tell us about the past.

The NHS Dashboard Obsession

• In the NHS, we often rely on dashboards describing the past to inform decisions about the future.
• This is inherently limited.
  • Many factors cause the future to differ from the past
  • Decision-making relies heavily on human judgment
  • Difficult to scale across thousands of cases
• We need better approaches to prediction

Traditional Approaches

• Rules-based systems - Predictions based on explicit rules (IF condition THEN action).
  • If diabetes AND age > 65 AND previous admission, flag as high risk
  • Works well when rules are known and context is simple, but struggles with complexity and scale.
• Statistical methods - Traditional statistical methods like regression.
  • Model readmission probability based on patient characteristics.
  • We need something that can be automated and applied at scale.

What is Machine Learning?

• Machine learning builds systems that learn patterns from data to make predictions about the future.
• It blends statistical methods and software development principles to solve problems of complexity and scale.
• It can produce highly effective predictions, applicable at scale, and deployable in the real world.

Why Use Machine Learning

• Handles complexity
  • Many variables, non-linear relationships
  • Finds interactions you wouldn’t think to test
• Scales effectively
  • Same approach works with 10 or 10,000 features
  • Predictions can be automated and applied widely
• Improves with data
  • More examples = better performance
  • Adapts to changing patterns

What Machine Learning Is Not

• ML is not magic - It still requires good data and domain knowledge.
• ML is not always better - Sometimes traditional approaches work fine.
• ML is not (necessarily) a black box - Many models are interpretable, and we can understand why they make their predictions.
• ML not replacing analysts - It’s a tool that extends what you can do. Human judgment still matters.

Healthcare Applications

• ML works with structured data across many contexts:
  • Predicting future admissions from past admissions and primary care data
  • Allocating resources efficiently by forecasting future demand
• ML also works well with unstructured data:
  • Diagnosing diabetic retinopathy using eye scans
  • Extracting predictions from handwritten clinician notes

How Does Machine Learning Work?

• The core idea:
  • Show the system labeled examples
  • The system learns patterns from these examples
  • Then predicts outcomes for new examples
• This is called supervised learning.

Supervised Learning

• Training a model on data where the outcome is already known and labeled, to predict outcomes on new data.
• You provide:
  • Features (input variables): age, diagnosis, lab results
  • Target (what to predict): readmitted yes/no
• The model learns the relationship between the features and the target.

The Basic Workflow

1. Get data with examples and labels
2. Split into training and testing sets
3. Train a model on training data
4. Make predictions on test data
5. Evaluate how well it performed

Thank You!

Contact:

Code & Slides:

/NHS-South-Central-and-West/code-club

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