Suppose we know the probabilities of a person having diabetes and a person having retinopathy independent of having diabetes and also we know the conditional probability of a person being diabetic if they have retinopathy. We can calculate the conditional probability of a person having (or getting) retinopathy given that they are diabetic using which formula?

What are the two schemes or methods for inference engine functionality described in the text? 1. 2.

Assume your CDSS has the following if-then rules: 1. If patient is diabetic     Then Check for retinopathy 2. If check for retinopathy is true     Then schedule eye exam with ophthalmologist Now suppose a new patient comes into the practice and states that they have had an ophthalmology exam the previous week.  This would then activate rule 2’s Then part, which in turn could cause a CDSS to ask if this was to check for retinopathy, which in turn would activate the Then portion of rule 1.. This is an example of what type of rule chaining?

For the sets: SET1 = {abdominal pain, nausea, jaundice} SET2 = {diabetes, food poisoning, flu} SET3 = {runny nose, fever, nausea} SET4 = {low blood sugar, fever, diaphoresis, dizziness, runny nose} What is the intersection of Set 1 ∩ ( Set 4 ∩ Set 3 )?

Assume that a CDSS developer is using both the clinical literature and expert clinician knowledge to build a knowledge base for a new CDSS.  What is a problem identified in the text for relying on expert opinion to drive the CDSS’s decisions?

What is the name of the Set Theory method used to tackle decision making problems when uncertainty plays a role?

Quinones 

The delivery from one NADH to the electron transport chain can generate how many ATP?

The final electron acceptor of the electron transport chain is 

Enzymes cannot be reused by the cell.