Based on my prior knowledge, premature atrial contractions in the anamnesis (nr_01) can be an indicator of heart-related issues. To determine if the myocardial infarction complications data of this patient shows chronic heart failure, we need to analyze how nr_01 relates to the target variable.

Using the given description, we can assume that the feature nr_01 is a categorical variable with two categories: 'no' and 'yes'. To analyze the relationship, we need to examine the distribution of these categories in relation to the target variable.

Based on the analysis, the dictionary for the relationship between feature nr_01 and chronic heart failure (target variable) can be generated as follows:

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

Since the feature nr_01 specifically represents premature atrial contractions in the anamnesis, it is reasonable to assume that if this feature is present (indicated by 'yes'), it may be related to chronic heart failure. Therefore, the only relevant value for the 'yes' target class is 'yes'. 

For the 'no' target class, since we are interested in determining chronic heart failure, we can assume that if nr_01 does not contain premature atrial contractions, it is unlikely to be related to chronic heart failure. Hence, the only relevant value for the 'no' target class is 'no'.

Note: If there are additional values for nr_01 that are hard to predict or not explicitly defined, we do not include them in the dictionary. However, make sure that each list for both target classes is not empty.