Based on prior knowledge, we can analyze the relationship between the feature "GEPAR_S_n" and the task of determining if the patient has chronic heart failure.

If the patient has chronic heart failure, it is possible that they are taking anticoagulants in the ICU to manage their condition. Therefore, we would expect to see a higher proportion of "yes" values for the "GEPAR_S_n" feature in the target class "yes" (indicating chronic heart failure).

On the other hand, if the patient does not have chronic heart failure, they may not require anticoagulants in the ICU as a result. This suggests that the proportion of "no" values for the "GEPAR_S_n" feature could be higher in the target class "no".

To generate the dictionary, we need to analyze the distribution of "GEPAR_S_n" values for each target class in the myocardial infarction complications data:

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

Keep in mind that this analysis assumes that patients without chronic heart failure are less likely to use anticoagulants in the ICU ("no" values are more common for target class "no"). However, it is always important to consider other factors and conduct further analysis to validate the relationship between the feature and target variable.