Based on prior knowledge, we can analyze the relationship between the feature "ASP_S_n" (use of acetylsalicylic acid in the ICU) and the target task of determining whether the patient has chronic heart failure.

If we assume that acetylsalicylic acid can be used to prevent myocardial infarction complications, then it is possible that patients who use acetylsalicylic acid (ASP_S_n = "yes") might have a reduced risk of chronic heart failure compared to those who do not use it (ASP_S_n = "no").

To create the dictionary with specific details, we can go through the myocardial infarction complications data and identify the possible values of "ASP_S_n" for both target classes ("yes" and "no"). We should not include any values of "ASP_S_n" for which there are no data available.

Here is the dictionary with the specific details:

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

In this case, it seems that the values of "ASP_S_n" are straightforward and there is no need for further categorization or detailed analysis. The "ASP_S_n" values for the target class "no" only include "no", indicating that patients without chronic heart failure do not use acetylsalicylic acid. Similarly, the "ASP_S_n" values for the target class "yes" only include "yes", indicating that patients with chronic heart failure do use acetylsalicylic acid.

Please note that in some specific cases, the relationship between a feature and the target may require more in-depth analysis or interpretation. However, based on the provided information, this is the analysis and dictionary that can be generated for the given feature and task.