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

Based on the feature description and the task, it appears that the feature "LID_S_n" represents the use of lidocaine in the ICU. To determine if the patient has chronic heart failure, we need to analyze the relationship between the use of lidocaine and the presence of chronic heart failure.

In this case, the feature "LID_S_n" is a categorical variable with two possible categories: "yes" and "no". It seems that the feature does not provide information about the presence or absence of chronic heart failure directly. Therefore, we cannot make a definitive prediction of chronic heart failure based solely on the "LID_S_n" feature.

However, if we assume that the use of lidocaine can potentially be associated with chronic heart failure, we can create a dictionary indicating the possible values of the feature "LID_S_n" for each target class.

In this case, since it is not stated how the "LID_S_n" feature relates to the presence or absence of chronic heart failure, we include all the possible values of the "LID_S_n" feature, but with the understanding that it may not provide predictive power for the task.

Therefore, the dictionary would be as follows:

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

Please note that this analysis assumes a lack of prior knowledge about the specific dataset and the relationship between the feature and target.