Based on prior knowledge, we can analyze the relationship between the feature "B_BLOK_S_n" (Use of beta-blockers in the ICU) and the presence of chronic heart failure in a patient with myocardial infarction complications.

To establish this relationship, we need to examine the distribution of the values of "B_BLOK_S_n" for patients with and without chronic heart failure. Let's assume that the target class "yes" indicates the presence of chronic heart failure, while the target class "no" indicates the absence of chronic heart failure.

Here is an example analysis based on hypothetical data:

For target class "no":
- Values of "B_BLOK_S_n" for patients without chronic heart failure: ['no', 'no', 'no']

For target class "yes":
- Values of "B_BLOK_S_n" for patients with chronic heart failure: ['yes', 'yes']

Based on this analysis, the dictionary representation would be:

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

Please note that this is a hypothetical analysis and the actual distribution of "B_BLOK_S_n" values for each target class may vary based on the dataset used.