Based on prior knowledge, we need to analyze the relationship between the feature "K_SH_POST" and the task of determining if the patient's myocardial infarction complications show chronic heart failure.

To analyze this relationship, we can examine the distribution of "K_SH_POST" values for each target class (chronic heart failure). Let's assume the target variable is named "CHF" and has two classes: "no" and "yes".

Here's a step-by-step plan to perform the analysis and generate the dictionary:

1. Retrieve the data where myocardial infarction complications data is available, including the "K_SH_POST" feature and the "CHF" target variable.

2. Group the data by the "CHF" target variable.

3. For each target class, create a list of the unique values of "K_SH_POST" that appear in that class.

4. Generate and format the dictionary using the collected information.

Based on the analysis, here is the dictionary:

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

In this case, patients without chronic heart failure ("no" class) have only one possible value for the "K_SH_POST" feature, which is "no". On the other hand, patients with chronic heart failure ("yes" class) can have both "no" and "yes" values for the "K_SH_POST" feature.