The Building Blocks of Information Are Selections - Let's Define Them Globally!

Digital information consists of sequences of numbers that are selections. So far, these are defined by context. We can globalize this by using an efficient global pointer (UL) as "context". The article explains new globally identified and defined "Domain Vectors" (DVs) for transporting digital information. They have the structure "UL plus sequence of numbers", where UL is an efficient identifier and global pointer (link) to the unified online definition of the sequence of numbers. Thus, the format of the number sequence and its meaning is defined online. This opens up far-reaching new possibilities for the efficient exchange, comparison and search of information. It can form the basis for a new global framework that improves the reproducibility, search, and exchange of data across systems, borders, and languages.

Reproducible Transport of Information.

Reproducible information is important in science, medicine and other professional fields. Repeating the same experiment with measurement should yield the same information as the result. This original information should also be transported digitally in reproducible form, as a globally well-defined sequence of numbers. The article explains that "Domain Vectors" (DVs) with the structure "UL plus sequence of numbers" are well suited for this purpose. "UL" is an efficient link to the online definition of the sequence of numbers. DVs are globally comparable and searchable and have other important advantages. It is concluded that DVs can fill an important gap in the digital representation of information.

Information Is Selection-A Review of Basics Shows Substantial Potential for Improvement of Digital Information Representation.

Any piece of information is a selection from a set of possibilities. In this paper, this set is called a "domain". Digital information consists of number sequences, which are selections from a domain. At present, these number sequences are defined contextually in a very variable way, which impairs their comparability. Therefore, global uniformly defined "domain vectors" (DVs), with a structure containing a "Uniform Locator" ("UL"), referred to as "UL plus number sequence", are proposed. The "UL" is an efficient global pointer to the uniform online definition of the subsequent number sequence. DVs are globally defined, identified, comparable, and searchable by criteria which users can define online. In medicine, for example, patients, doctors, and medical specialists can define DVs online and can, therefore, form global criteria which are important for certain diagnoses. This allows for the immediate generation of precise diagnostic specific statistics of "similar medical cases", in order to discern the best therapy. The introduction of a compact DV data structure may substantially improve the digital representation of medical information.

Collection of Medical Original Data with Search Engine for Decision Support.

Medicine is becoming more and more complex and humans can capture total medical knowledge only partially. For specific access a high resolution search engine is demonstrated, which allows besides conventional text search also search of precise quantitative data of medical findings, therapies and results. Users can define metric spaces ("Domain Spaces", DSs) with all searchable quantitative data ("Domain Vectors", DSs). An implementation of the search engine is online in http://numericsearch.com. In future medicine the doctor could make first a rough diagnosis and check which fine diagnostics (quantitative data) colleagues had collected in such a case. Then the doctor decides about fine diagnostics and results are sent (half automatically) to the search engine which filters a group of patients which best fits to these data. In this specific group variable therapies can be checked with associated therapeutic results, like in an individual scientific study for the current patient. The statistical (anonymous) results could be used for specific decision support. Reversely the therapeutic decision (in the best case with later results) could be used to enhance the collection of precise pseudonymous medical original data which is used for better and better statistical (anonymous) search results.

Survey of oral health-related quality of life among skeletal malocclusion patients following orthodontic treatment and orthognathic surgery.

OBJECTIVES: The goal of this work was to conduct a survey of oral health-related quality of life (OHRQoL) among skeletal malocclusion patients who had undergone orthodontic treatment combined with orthognathic surgery. PATIENTS AND METHODS: The study included 28 patients who had undergone orthodontic therapy combined with orthognathic surgery (surgical repositioning osteotomy) to treat skeletal malocclusion. OHRQoL was assessed based on a 14-item German version of the Oral Health Impact Profile (OHIP-G14) and an additional questionnaire developed by the authors to evaluate aspects of quality of life specific to skeletal malocclusion patients. RESULTS: The mean OHIP-G14 score demonstrates that the malocclusion patients have a lower OHRQoL than the general population. The information collected via our skeletal malocclusion questionnaire correlates with the patients' perceived esthetic and functional outcomes of treatment as well as their psychological state and social interactions. Satisfaction with treatment outcomes and improved social interactions correlated with high OHRQoL scores. High OHRQoL scores significantly correlated with enhanced well-being and the intensification of social contacts. CONCLUSION: Our skeletal malocclusion questionnaire provides useful additional information on specific aspects of skeletal malocclusion patients, mapping in a detailed fashion aspects of function, esthetics, psychological condition, and social interactions. OHIP-G14 scores correlated negatively with OHRQoL.

Standardized vectorial representation of medical data in patient records.

In this paper standardized vectorial (quantitative) representation of medical data is suggested for use in patient records. Vectorial representations are (as sequences of numbers) language independent, precise, directly comparable, and they allow advanced evaluation, e.g. similarity calculation using well defined distance functions. It is possible to search for a patient with a certain combination of diagnostic parameters on the Web records of patients with similar parameters. The information about chosen treatments and treatment outcome at these patients can be used anonymously or pseudonymously for decision support. Because patient records from all countries can be compared, in the long run this could open systematic access to a very large wealth of clinically relevant information. Here the technical principle is described and illustrated by examples (e.g. similarity search of heart sounds). Previously published material is integrated in parts for explanation of the motivation and background.

A searchable patient record database for decision support.

UNLABELLED: We describe a searchable patient record database for decision support. It contains medical histories of real but pseudonymous patients with patterns of diagnosis, chosen treatment, and outcome. To be searchable, the patterns contain a feature vector (for similarity search by calculating distances) and a globally unique "pattern name" which identifies the kind of data which are represented by the feature vector. Patterns with the same pattern name are directly comparable; they represent the same kind of data. For pattern selection the database provides a growing well-structured list of initial diagnoses with associated input masks. PROCEDURE: The doctor can assume that the database contains patients similar to the current patient if he finds his initial diagnosis in the list. Clicking on it opens an associated input mask which requests specific further data for finer differentiation. After input a searchable pattern group is built from the provided data, and used to search for histories of patients with similar fine diagnostics, and for the most successful treatment decisions at these patients. This information can be very valuable for deciding the treatment of the current patient. Because the database can collect patient histories from all countries, in the long run this could open access to a wealth of experience which by far exceeds the capacity of a today's doctor.

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

UNLABELLED: 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.