Promedas is the result of many years of development by researchers such as Wim Wiegerinck, led by Martijn Leisink and Bert Kappen of Radboud University Nijmegen in the Netherlands. It now contains Bayesian inference rules that cover a huge area of the medical domain. The Bayesian approach offers a major advantage. In the words of Martijn Leisink: “The clear advantage of the probabilistic representation is the natural way that different diseases or findings influence each other. If one finding leads to two different diagnoses these are difficult to merge in a rule based system. At least one additional rule is necessary. In the probabilistic setting, making use of the basic probabilistic rules, it is immediately clear how to combine evidence and variables.”
The success of Promedas relies on its careful structuring of the dependencies (which findings imply which diseases). It organises its information as a tree in a three-layered noisy “OR” model. The layers of the tree correspond to risk factors such as occupation or drug use, possible diseases and the tests and symptoms. Each node in the network (either a risk factor, disease or test result) is linked according to specific probabilities of cause and effects, with some risk factors likely to cause some diseases, and some diseases likely to cause some symptoms and results of tests.
As more patient records become stored electronically, this will become less of an issue but in the near future the use of Promedas is likely to be restricted to those mysterious cases where the doctor needs some new ideas. It’s an important role, for specialists who have chosen to focus on one specific area of medicine may become less knowledgeable about other areas. The vast amount of expert knowledge in Promedas means that all physicians of all specialisations will have access to the same up-to-date specialist information. There can be no doubt that a list of possible diagnoses of varying probabilities for a patient makes an excellent decision support system, for it may suggest rarer alternatives that could be confirmed by additional tests.
The first large-scale trial will begin in early 2008 at the University of Utrecht, Netherlands. While results are still not perfect, initial experiences by doctors are very positive. In the words of Dr Jan Neijt of the University of Utrecht (the physician who has so far provided all of the medical knowledge for Promedas), “…used with reason it is always helpful… This is the future for medicine with all the sub specialists. They need a program that looks further away than their sub specialisation.”
By representing each node as a “noisy OR” Promedas simplifies and speeds up the inference process, making the assumption that each cause can behave independently but multiple causes combine to make outcomes more probable. With a given model and data about the risk factors and test results, the probabilities of different diseases can then be inferred.
As with all such systems, often the main bottleneck is simply the input of data. Many patient records are incomplete or are not in the right kind of format to enable easy input, and typing in the results of tens or hundreds of different observations and tests can be laborious.
Perhaps one day systems such as Promedas will become as ubiquitous as the stethoscope, with their databases updated as new medical findings are published. We can never remove uncertainties from medicine, but with decision support systems we can ensure that all decisions made by our doctors are as well-informed as possible.
Resources: Promedas (including online demonstraton): http://www.promedas.nl/ Promedas publications: http://www.snn.ru.nl/nijmegen/publicatie.php3?projekt=Promedas PASCAL: http://www.pascal-network.org/