Design of a Global Medical Database which is Searchable by Human Diagnostic Patterns

Wolfgang Orthuber*, 1, Gunar Fiedler2, Michael Kattan3, Thorsten Sommer1, Helge Fischer-Brandies1
1 Department of Orthodontics at University of Kiel, Germany
2 Institute of Informatics at University of Kiel, Germany
3 Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA

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open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Orthodontics at University of Kiel, Germany; E-mail:


We describe a global medical database which is designed for efficient evaluation. It allows language independent search for human diagnostic parameters. Core of the database is a fully automated electronic archive and distribution server for medical histories of real but anonymous patients which contain patterns of diagnosis, chosen treatment, and outcome. Every pattern is represented by a feature vector which is usually a sequence of numbers, and labeled by an unambiguous "pattern name" which identifies its meaning. Similarity search is always done only over patterns with the same pattern name, because these are directly comparable. Similarities of patterns are mapped to spatial similarities (small distances) of their feature vectors using an appropriate metric. This makes them searchable. Pattern names can be "owned" like today domain names. This facilitates unbureaucratic definition of patterns e.g. by manufacturers of diagnostic devices. Application: If there is a new patient with certain diagnostic patterns, it is possible to combine a part or all of them and to search in the database for completed histories of patients with similar patterns to find the best treatment. Confinement of the result by conventional language based search terms is possible, and immediate individual statistics or regression analyses can quantify probabilities of success in case of different treatment choices. Conclusions: Efficient searching with diagnostic patterns is technically feasible. Labeled feature vectors induce a systematic and expandable approach. The database also allows immediate calculation of individual up to date prediction models.