Based on my prior knowledge, the feature "K_SH_POST" represents whether the patient had cardiogenic shock at the time of admission to the intensive care unit. To analyze the relationship between this feature and the presence of chronic heart failure (target), we need to consider the distribution of "K_SH_POST" categories for each target class.

Here is the analysis:

For the "no" class (no chronic heart failure):
- Possible values of feature "K_SH_POST": ['no', 'yes']
- Patients without chronic heart failure are likely to have a value of "no" for the "K_SH_POST" feature.

For the "yes" class (chronic heart failure):
- Possible values of feature "K_SH_POST": ['no', 'yes']
- It is possible for patients with chronic heart failure to have either a value of "no" or "yes" for the "K_SH_POST" feature.

Based on this analysis, I can generate the following dictionary:

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

This dictionary indicates that patients without chronic heart failure ("no" class) are likely to have a value of "no" for the "K_SH_POST" feature. On the other hand, patients with chronic heart failure ("yes" class) can have either a value of "no" or "yes" for the "K_SH_POST" feature.