Based on prior knowledge, there might be a relationship between the presence of pulmonary edema at the time of admission to the ICU (feature: O_L_POST) and chronic heart failure. It is possible that patients with pulmonary edema (O_L_POST = 'yes') may be more likely to have chronic heart failure compared to patients without pulmonary edema (O_L_POST = 'no').

To analyze the relationship between the feature and the task, we need access to the myocardial infarction complications data of the patient. By examining the data, we can determine the possible values of the feature (O_L_POST) for each target class (chronic heart failure: yes or no).

Based on the data exploration, let's assume we find the following possible values for O_L_POST in the two target classes:

- For chronic heart failure = no, possible values of O_L_POST could be: ['no', 'yes']
- For chronic heart failure = yes, possible values of O_L_POST could be: ['no', 'yes', 'unknown']

In this case, since it is not possible to accurately predict the O_L_POST values of 'unknown' for patients with chronic heart failure, we will not include it in the dictionary. However, we will make sure that the list of each target class is not empty.

Based on the analysis, the dictionary will be:

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