Based on my prior knowledge, the use of lidocaine in the ICU (LID_S_n) could potentially have a relationship with the presence or absence of chronic heart failure in the myocardial infarction complications data of a patient.

To analyze this relationship, we need to examine the distribution of LID_S_n values for each target class (yes and no).

Here is a possible analysis:

- For patients who do not have chronic heart failure (target class: no), the possible values of LID_S_n could include 'yes' or 'no'. It is possible that some patients without chronic heart failure may have received lidocaine in the ICU, while others may not have.

- For patients who do have chronic heart failure (target class: yes), the possible values of LID_S_n could also include 'yes' or 'no'. Similarly, some patients with chronic heart failure may have received lidocaine in the ICU, while others may not have.

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

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

Keep in mind that this analysis assumes there is no missing data or other potential confounding factors that may impact the relationship between LID_S_n and chronic heart failure. Additionally, it is worth noting that the values "yes" and "no" in the dictionary are represented as strings.