Based on prior knowledge, beta-blockers are commonly used to treat chronic heart failure. Therefore, it is likely that patients with chronic heart failure would be prescribed beta-blockers in the ICU.

To analyze the relationship between the feature "B_BLOK_S_n" (Use of beta-blockers in the ICU) and the task of determining if the patient has chronic heart failure, we can examine the distribution of the feature values for each target class.

Here is the analysis:

- If the patient shows chronic heart failure (target: "yes"), it is likely that they were prescribed beta-blockers in the ICU. Therefore, we would expect to see a high proportion of "yes" values in the feature "B_BLOK_S_n" for this target class.

- If the patient does not show chronic heart failure (target: "no"), it is less likely that they were prescribed beta-blockers in the ICU. Therefore, we would expect to see a lower proportion of "yes" values in the feature "B_BLOK_S_n" for this target class.

Based on this analysis, we can create the following dictionary:

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

Note that the values in the "no" class should only include "no" because it is less likely for patients without chronic heart failure to be prescribed beta-blockers. However, for the "yes" class, we include both "no" and "yes" values because it is likely for patients with chronic heart failure to be prescribed beta-blockers in the ICU.

This dictionary represents the possible values of the feature "B_BLOK_S_n" for each target class.