Content:

Dr. Greenes lectures introduce us to the world of clinical decision making. On Monday he talked about types of models.  For example, physical models, flow charts and decision trees.  He then went to explain to us the Bayesian type models.  These models have a backbone of probability. A decision tree can be build with every branch having a different probability which can help decide a clinician what route to take with a patient.  Some advantages to models are that they insight for understanding.  Although Bayesian models seems useful, in practice it is very hard to get probability estimates.

On Wednesday, Dr. Greenes continued his introduction to clinical decision making by first stating that decision science is a combination of different domain such as economics, statistics, andpsychology . Thereafter he have a full explanation about decision trees. Decision trees include decision nodes, chance nodes, probabilities and utilities.  He explain a measure that is used when creating a decision tree: Quality Adjusted Life Years (QALY).  He went on to introduce us the conditional probabilities and it applications

Posted by