Identifying patients at risk

Is this AI?

YiWen Hon

10 October 2024

AI could help identify high-risk heart patients1

The University of Leeds has helped train an AI system called Optimise, that looked at health records of more than two million people.

…

Of those two million records that were scanned, more than 400,000 people were identified as being high risk for the likes of heart failure, stroke and diabetes.

How it works

  • The input: Health records
  • Health records can be structured or unstructured
    • Structured: can be stored in a table
    • Unstructured: can’t be stored in a table, different shapes/sizes (e.g. text, audio, images)

Example of structured data in health records

ID BMI Age IMD Decile Smoker Blood Pressure
1 17 49 3 1 110/70
2 25 67 1 1 129/70
3 20 39 8 0 140/90
4 28 81 6 0 130/85
5 29 41 4 0 120/80

Data is consistent within each column in the table.

Example of unstructured data in health records

ID Notes
1 Shortness of breath
2 Patient attended clinic following one week of fever, vomiting, and abdominal pain.

The length of each sentence is different - data not consistent.

A simple approach to classifying data: KNN

Clustering algorithms like K Nearest Neighbours (KNN) are on the more basic end of the scale, requiring very little computational power.

Example of k-nearest neighbour classification, with red triangles representing one class, blue squares representing another, and a new datapoint as a green circle which has two close red triangle neighbours and one blue square neighbour.1

A simple approach to classifying data: Decision Tree

Example of a decision tree1

There are many different models out there! 🥴

Complex diagram showing a decision tree for choosing the right estimator for different machine learning problems1

What makes a model simple or complex?

  • There are dozens of different algorithms out there
  • Each algorithm has different strengths and weaknesses
  • What makes a model simple or complex is the amount of computational power required and how much the model needs to “learn” - how many parameters there are

Is the input or the computation complex?

“We used UK primary care EHR data from 2,081,139 individuals aged ≥ 30 years…

We trained a random forest classifier using age, sex, ethnicity and comorbidities (OPTIMISE).”1

Pros and cons of simple “A.I.” approaches

Pros:

  • Simple models are more easily explained
  • Can sometimes find new patterns in the data

Cons:

  • The quality of the data determines the quality of the model
  • Not able to handle very complex tasks

🚩 Issues to look out for 🚩

  • How complex is the input, or the computational approach?
  • How is the model’s performance measured?
  • Does the model get updated?
  • Where did the data come from?
  • Have issues of bias or ethics been considered?