Based on prior knowledge, we can analyze the relationship between the feature "LID_S_n" and the target variable "chronic heart failure" using the myocardial infarction complications data of the patient.

Analysis:
- LID_S_n represents the use of lidocaine in the ICU, with two possible categories: 'yes' or 'no'.
- Lidocaine is a medication that can be used to treat certain heart rhythm disorders and provide anesthesia.
- Chronic heart failure refers to a long-term condition where the heart is unable to pump blood effectively.

Based on this information, we can hypothesize the following relationship between the feature and the target:
- If lidocaine is used in the ICU ('LID_S_n' = 'yes'), it might indicate a more severe myocardial infarction complication, which could potentially lead to chronic heart failure.
- If lidocaine is not used in the ICU ('LID_S_n' = 'no'), it might suggest a milder complication or alternative treatment options, indicating the absence of chronic heart failure.

To create the dictionary, we need to examine the different values of 'LID_S_n' for each target class:

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

In this case, the values for 'LID_S_n' are straightforward, with 'no' representing the absence of lidocaine usage in the ICU and 'yes' representing its usage. Moving forward, additional analysis and consideration of other features could provide more insights and opportunities for defining relationships accurately.