Mathematical Modelling with Chernobyl Registry Data (eBook, PDF)

Registry and Concepts
Redaktion: Morgenstern, Wolfgang; Schettler, Gotthard; Tsyb, Anatoli F.; Michalski, Anatoli I.; Ivanov, Victor K.

• Format: PDF

Produktbeschreibung

In 1993 three institutes belonging respectively to the Heidelberg Academy for Humanities and Sciences, to the Russian Academy of Sciences, and to the Russian Academy of Medical Sciences, launched a cooperative research project. The main aim of the project is to develop mathematical methodologies needed for better information support in health policy decision-making in the area of primary health care provision for the Russian populations affected by radiation due to the Chernobyl accident. At present, the major data basis of the project is the Russian National medical and Dosimetric (Chernobyl) Registry administered at the Medical Radiological Research Centre, Obninsk. The book describes the general mathematical approaches to the project and the given conditions of the registry.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 110
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783642800108
• Artikelnr.: 53149270

Autorenporträt

In 1993, three institutes, belonging respectively to the Heidelberg Academy for the Humanities and Sciences, to the Russian Academy of Sciences, adn to the Russian Academy of Medical Sciences,launched a cooperative research project. The main aim is to develop mathematical methodologies needed for a metter information support in health policy decision-making in connection with the Chernobyl accident.

Inhaltsangabe

1 The Project.
1.1 Objectives.
1.2 Present Outcomes.
2 The Russian National Medical and Dosimetric Registry.
2.1 The Primary Registration and Examination Forms.
2.2 Data Base and Data Processing.
2.3 The Reconstruction of Absorbed Radiation Doses.
3. Health Status Indicators.
4. Mathematical Concepts.
4.1 The Demand for a New Methodology.
4.2 Semi
Markov Modelling.
4.3 Heterogeneity.
4.4 Unobserved States and Variables.
4.5 Multiple Risks.
4.6 Model Selection on Empirical Data.