Based on prior knowledge, we can analyze the relationship between the feature "lat_im" and the target variable "chronic heart failure" in patients with myocardial infarction complications.

From the given feature description, "lat_im" represents the presence of a lateral myocardial infarction. It is a categorical variable with the following possible categories:

- QRS has no changes
- There is no infarct in this location
- QRS is like Qr-complex
- QRS is like QS-complex
- QRS is like QR-complex

To determine if the patient shows chronic heart failure, we need to analyze if certain values of "lat_im" are more prevalent in cases with chronic heart failure or without chronic heart failure.

Let's assume the target variable "chronic heart failure" is binary, where 'yes' represents the presence of chronic heart failure and 'no' represents the absence of chronic heart failure.

To create the dictionary, we need to identify the possible values of "lat_im" for both classes:

```json
{
	"no": ["QRS has no changes", "there is no infarct in this location"],
	"yes": ["QRS is like Qr-complex", "QRS is like QS-complex", "QRS is like QR-complex"]
}
```

Note that we have only included the values for which we have information regarding their relationship to chronic heart failure. If there are other values for "lat_im" that are hard to predict or don't have enough information to make any assumptions, we can omit them from the dictionary. However, it is important to ensure that the list for each target class is not empty.