Based on prior knowledge, we can analyze the relationship between the feature "GEPAR_S_n" (Use of а anticoagulants in the ICU) and the presence of chronic heart failure (the target variable).

To determine whether the patient has chronic heart failure, we need to consider if there is any relationship between the use of anticoagulants in the ICU and the presence of chronic heart failure.

Since we only have two categories for the "GEPAR_S_n" feature ("yes" and "no"), we can analyze the distribution of these categories for each target class ("yes" or "no").

Let's now analyze the relationship between the feature and target variable and create the dictionary:

```json
{
	"no": ["yes", "no"],  
	"yes": ["no"]
}
```

Based on the analysis, we can see that both "yes" and "no" categories of the "GEPAR_S_n" feature can be present for the target class "no" (chronic heart failure). However, for the target class "yes" (indicating chronic heart failure), only the category "no" is present.

This implies that the presence of chronic heart failure is associated with the absence of anticoagulant use in the ICU (GEPAR_S_n = "no"). However, the presence of chronic heart failure does not exclude the possibility of anticoagulant use in the ICU (GEPAR_S_n = "yes").

It is important to note that included values are based on prior knowledge and may differ depending on the dataset being analyzed.