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Injury Prediction
--
There were a few great discussions here on Twitter about injury prediction over the last few days which got me thinking.
Here are my thoughts as a practitioner. A set of principles I tend to keep in mind.
Thread
⬇
Basics
--
Don't worry about injury prediction if an athlete doesn't lift twice a week.
Don't kill me yet...It's just a metaphor to make the point.
Just wanted to get that out of the way before we keep going!
Algorithms
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The algorithm, model, method or steps applied to the data have important practical implications. I'm not saying we have to go deep into the maths, but we should try to understand the overall workflow of the methods used. This will support our critical thinking.
Re Critical Thinking
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Part of our job is to disseminate research to inform our practice. Often companies will knock on our door to sell this new injury prediction product.
You can ask your expert friend for help, but you may as well learn and get better at it.
Transparency
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That links to the previous tweet. Show me the m̵o̵n̵e̵y̵ code.
There is no secret sauce. Show the code or ask for code. It is often a good idea to get others to have a look.
Is it reproductible? How does it test in different populations? etc.
Simplicity
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Less is more. Why? Two things come to my mind:
1. The risk of overfitting goes up as complexity increases (there are ways to deal with this I know, but still...)
2. Focusing on less metrics can help to learn faster about our interventions.
Start small.
Performance
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We don't need to have a perfect model. There is not such thing. Build your model, evaluate it, understand how it performs so you know how much trust to put on it. Many times, if it performs poorly, you may come up with ways to improve it
Keep iterating.
Machine Learning
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ML is buzzing. There're some powerful things out there. Majority open-source. All becoming more user-friendly.
Simple first - Fancy later. If a simpler model offers a similar performance (even if slightly worse) stay simple. It'll pay off later.
Causality
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When something correlates with risk, chances are that manipulating that something will impact the prediction scores. But this is not always the case. Most predictive models don't study causation, yet. Keep this in mind.
The practitioner's judgment is still relevant.
Probability
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Predictions are not (or should not be) binary. What we are doing is estimating risk (probability distribution), based on the available data.
All we can do is to add this information to our decision making process and use within context.
Check, Double Check
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Don't oversell.
Try to show (and explain) uncertainty.
Most of the time, especially early, it's better to be skeptical. Even if things look good.
Explainability
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Many models are black boxes.
Banks just want to know whether someone will pay back or not. The reasons behind are irrelevant.
But coaches have to go back to the athlete and say: "so this is what we're doing and why".
Understand how models predict may help.
Keep calm and...
--
The most popular algorithm on Kaggle - XGBoost - is 7 years old.
TensorFlow was released in 2015.
The SHAP python library is from 2018.
Data - still the biggest challenge - is more available and diverse now than 5 years ago.
We are making progress.
Standards
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The level of confidence is low. The research published so far does not help. It is in general, of very low quality. What can we do?
1. Identify the flaws
2. Keep learning
3. Lean on experts when we have to
This applies to both, researchers and applied practitioners
That's it
--
That's more or less it.
Hope that helps. Very polarized topic. Would love to hear what others use, ideas, feedback, etc...please feel free to share.