The described concepts relate to automated patient disposition. One example can receive a clinician's disposition for a patient. This implementation can perform parameter-based cluster analysis on the patient and a set of patients to identify a sub-set of the patients with which the patient has a high similarity. This example can also cause a graphical user interface to be generated that conveys parameters from the sub-set of the patients and the patient. BACKGROUND The present discussion relates to patient care. For instance, one profoundly fragile but consequential decision in hospitals is related to making decisions around the disposition of a patient. For example, such decisions can relate to when to discharge from the emergency department, versus admit, when to discharge from the hospital, when to transition from one part of the hospital to another, etc. Patient disposition is a complex and subtle decision with profound implications to the patient. One such scenario relates to “Failure to Rescue”. Failure to rescue refers to the phenomena where a patient is not transferred to the intensive care unit (ICU) soon enough and instead suffers cardiac or respiratory arrest on a non-ICU floor. Failure to Rescue is estimated to occur across the U.S. at a rate of almost 300,000 cases per year. In an attempt to remedy this phenomenon, today many emergency medicine rooms (EMRs) manually implement a series of rules to check whether a discharge might be dangerous. These rules may check for critical lab values or abnormal vital signs. The challenge is that there could be hundreds or thousands of rules that need to be written to cover the thousands of known lab tests available today. Yet when all those rules are written, there is also a factorial of interactions between those labs that may also be predictive of danger. Further, new laboratory tests and new knowledge on how to use those tests is entering healthcare at an exponential rate.