Equilibrium-AI-Fall Risk Prediction Guidance

Example

Gathering and standardizing patient demographics, medical histories, and physical test results.

Scenario

In a rehabilitation center, Equilibrium-AI collects data from newly admitted patients, including their age, history of falls, medication use, and balance test scores, to prepare for analysis.

Example

Developing machine learning models to assess fall risk.

Scenario

Equilibrium-AI uses patient data to train a model that predicts the likelihood of future falls. This involves selecting relevant features, like muscle strength and reaction time, and applying models like Random Forest or Gradient Boosting for prediction.

Example

Predicting fall risk percentages and evaluating functional capacity.

Scenario

For each patient, Equilibrium-AI calculates a fall risk percentage based on their data. This helps healthcare providers in tailoring individualized prevention plans, focusing on areas like balance training and strength exercises.

Example

Applying the algorithm in real-world settings and validating its predictions.

Scenario

After developing the fall risk prediction algorithm, Equilibrium-AI is tested in a clinical setting with real patient data to ensure its accuracy and reliability in predicting falls.