Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_06" and the target variable "chronic heart failure" (myocardial infarction complications data).

To determine whether the feature indicates chronic heart failure, we need to look for any patterns or trends in the data. Let's analyze the possible values of the feature "fibr_ter_06" for each target class:

For target class "no" (indicating no chronic heart failure):

- ["fibr_ter_06": "no"]: This value suggests that fibrinolytic therapy by Celiasum 500k IU was not administered. 

For target class "yes" (indicating chronic heart failure):

- ["fibr_ter_06": "yes"]: This value suggests that fibrinolytic therapy by Celiasum 500k IU was administered. 

Based on this analysis, we can create the dictionary:

```json
{
    "no": ["no"],
    "yes": ["yes"]
}
```
Please note that this dictionary only includes the possible values of the feature "fibr_ter_06" that are relevant to each target class.