Based on my prior knowledge, I will analyze the relationship between the feature "n_p_ecg_p_11" and the task of determining whether the myocardial infarction complications data of this patient shows chronic heart failure.

To perform this analysis, I would need access to a dataset that contains both the feature "n_p_ecg_p_11" and the target variable indicating whether the patient has chronic heart failure.

Assuming I have access to the data, I would first group the data based on the target variable (chronic heart failure) and then examine the distribution of the values for the feature "n_p_ecg_p_11" within each group.

Here is an example of how the analysis could be performed:

- Target class: no (indicating no chronic heart failure)
  - Possible values of feature "n_p_ecg_p_11": ['no', 'yes']

- Target class: yes (indicating chronic heart failure)
  - Possible values of feature "n_p_ecg_p_11": ['no', 'yes']

Based on this analysis, we can conclude that the feature "n_p_ecg_p_11" has two possible values ('no' and 'yes') for both target classes.

Using this information, we can create the required dictionary:

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

Please note that for this example, the possible values of the feature "n_p_ecg_p_11" are the same for both target classes. However, in a real dataset, the values might differ, and it would be important to include all possible values for both target classes in the dictionary.