Based on prior knowledge, we can analyze the relationship between the feature "O_L_POST" (Pulmonary edema at the time of admission to the intensive care unit) and the task of determining whether the patient shows chronic heart failure.

To analyze the relationship, we can look at the distribution of "O_L_POST" values for each target class. By examining the values of "O_L_POST" for patients with and without chronic heart failure, we can determine if any patterns or differences emerge.

Here is the analysis and the resulting dictionary:

- For patients without chronic heart failure:
  - "O_L_POST" values include: 'no'
  
- For patients with chronic heart failure:
  - "O_L_POST" values include: 'no', 'yes'

Based on this analysis, we can create the following dictionary:

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

In this dictionary, the target class "no" corresponds to the feature value 'no' for patients without chronic heart failure. The target class "yes" corresponds to both feature values 'no' and 'yes' for patients with chronic heart failure.

Please note that if there are other potential values for "O_L_POST" that are not specified in the dataset or are hard to predict, we do not include them in the dictionary to avoid adding unnecessary complexity.