AI Able to Predict the Level of Recovery Following a Stroke

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AI Able to Predict the Level of Recovery Following a Stroke


Background information:

What is a Neural Network?

What is Logistic Regression?

What is Random Forest?


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        In this article: AI to Predict Rehab Outcomes in Stroke Patients, computer scientists have developed a software program using a Deep Neural Network (DNN) model, logistic regression (LR), and random forest (RF) with the ability to predict motor function in stroke patients 6 months following a stroke. Although it is a primary research article, I do recommend that you give it a read as it provides a good primary example of how AI is being designed to serve the medical field.

    Scientists analyzed the data of 1,056 stroke patients aged 43-72 between January 2003 and January 2020 without other health complications. Using Python, TensorFlow 2.3.0, and sci-kit-learn toolkit 0.23.2, these machine learning (ML) algorithms were able to detect patterns from this input data using 14 different variables like age, sex, type of stroke, and more. The deep neural network, logistic regression, and random forest were able to accurately predict upper limb recovery with a 91%, 87%, and 88% accuracy respectively. For lower limb recovery, the algorithms were able to predict outcomes with an 82% (DNN), 76% (LR), and 80%(RF) accuracy. As a reference, 70-80% is considered to be an acceptable accuracy of prediction, 80-90% is considered to be excellent and above 90% is considered to be outstanding.

     A similar study (Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation - ScienceDirect) conducted in 2018 used random forest and logistic regression and were only able to predict rehabilitation outcomes with an 80% and 79% accuracy respectively. This means that the algorithm used to predict these outcomes are becoming increasingly refined and soon enough, we may be able to have these software programs predict outcomes with uncanny accuracy. 


   All in all, the accuracies of each of these predictive algorithms have been found to range from outstanding to excellent for upper limb recovery and excellent to acceptable for prediction of lower limb recovery. Being able to accurately predict motor abilities following a stroke allows clinicians to determine better rehabilitation programs and better counsel their patients. For example, if the program predicts that a patient will undergo a full recovery, doctors can confidently ease patients', as well as their family's, worries. Additionally, as algorithms are being continuously improved, one day they could serve to predict abnormalities occurring within the body better than human specialists which would save thousands of lives.


What do you guys think are potential drawbacks/implications to relying on AI to determine rehab programs?


What other ways could AI like this be used to potentially innovate the field of medicine?


What other problems could these algorithms potentially be used for?

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