Based on my prior knowledge, the use of beta-blockers in the ICU (B_BLOK_S_n) can have an impact on chronic heart failure in patients with myocardial infarction complications. Beta-blockers are commonly prescribed medications for heart conditions, including chronic heart failure. Therefore, it is possible that patients who are prescribed beta-blockers may have a lower likelihood of developing chronic heart failure.

To analyze the relationship between the feature B_BLOK_S_n and the task of determining if the patient shows chronic heart failure, we need to examine the distribution of B_BLOK_S_n values for each target class (yes or no).

Here is the dictionary based on the analysis:

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

In this case, since the feature B_BLOK_S_n is a categorical variable with two distinct categories, "no" and "yes", we can see that the presence of beta-blockers in the ICU (B_BLOK_S_n) is only relevant if the value is "yes". Therefore, the possible values for the target class "no" are just ["no"], indicating that patients who do not develop chronic heart failure do not have beta-blockers in the ICU.

On the other hand, the possible values for the target class "yes" are ["yes"], indicating that patients who develop chronic heart failure may have beta-blockers in the ICU.

Please note that this analysis assumes that the dataset accurately represents the relationship between the feature and the target variable and was obtained from a reliable source.