Implementing ML for patient risk stratification

ML algorithms can identify patterns in patient data that correlate with a medical event. For example, ML models can look at the observations, medical conditions, medications, and demographic information of chronic diabetes patients to determine whether they are at risk of kidney failure. As a result of this early identification, those patients can be provided additional intervention or medications that would prevent this from happening. There are several factors to keep in mind when choosing the right ML-based approach to stratify the patient population. These guidelines will help you define a problem and choose the right datasets and ML algorithms appropriate for the dataset and the problem. They will also help you determine whether the generation of a risk profile is useful or not based on how actionable the insights are. Here are a few important points to keep in mind:

• Problem Definition and Dataset: The first step to keep...