Medical case-based reasoning for detecting inborn metabolic defects

system is a data model that contains a large number of pre-defined attributes like a controlled vocabulary. A set of these attributes describes a case. Within its description are varied information about main data, laboratory parameters, symptoms, therapy information and molecular genetic data. The controlled vocabulary is maintained by a system administrator that adds new, not existing attributes on demand. This procedure ensures the prevention of using homonymous or synonymous medical terms in the case description. The basis of our system is a relational database system that stores the cases in form of patient case reports. These reports are collected by health professionals, which submit the data of their currently treated patients with appropriate diseases. An alternative way to expand the case base is the collection of relevant medical publications. Scientific researches can use our system by a web-based graphical user interface that is connected to the CBR module, receives user requests and presents its results. Furthermore, the database is used by other software applications. The medical case-based reasoning module controls the CBR functions and executes queries on the database. Our system implements a case-match CBR that searches for similar cases and leaves the adaptation of the proposed solution to the user. The implemented retrieval phase of the CBR cycle selects a number of potential solution candidates from the case base of RAMEDIS. When this phase uses a lot of cases, the processing may result in a high computational complexity. Therefore, a pre-selection of cases is useful. This is done by using the MAC/FAC model [3] that pre-selects in a first step (many are called) a number of cases from the case base by relational retrieval. The typically used indexing mechanisms and queries of this pre-selection are based on the structures and methods of relational database systems. A case becomes an element of the candidates set if there is at least one attribute of the case that matches to the problem situation (partial identity). In the following phase (few are chosen), the similarity of the pre-selected cases and the current problem situation is calculated. This calculation bases on the weighted average of the single similarities and on different distance functions depending on the attribute types like Euclid distance or distance table. Results As described before, the access to the system is enabled by a graphical user interface via WWW at http://www.ramedis. de/cbr/. This interface is based on dynamically generated web pages that can be presented by every common web browser like Mozilla Firefox or Internet Explorer. The user selects a number of features from the controlled vocabulary to describe the problem situation most suitable. Afterwards, the retrieval module runs the case-match CBR algorithm on the case base and calculates a list of similar cases. Detailed information on single similarities for each feature are given to explain the calculation process to the user. With this list, the user can select solution candidates and use diagnosis and therapy information to consider the suitability of the selected case in comparison to the current problem situation.