c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 6 ( 2 0 1 2 ) 114–125 j o ur nal homep age : w ww.intl.elsevierhealth.com/journals/cmpb Arden2ByteCode: A one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform Matthias Gietzelt a , Ursula Goltz b , Daniel Grunwald b , Malte Lochau b , Michael Marschollek a,1 , Bianying Song a , Klaus-Hendrik Wolf a,* a Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig-Institute of Technology and Hannover Medical School, Muehlenpfordtstr. 23, 38106 Braunschweig, Germany b Institute for Programming and Reactive Systems, University of Braunschweig-Institute of Technology, Muehlenpfordtstr. 23, 38106 Braunschweig, Germany a r t i c l e i n f o Article history: Received 10 November 2011 Accepted 11 November 2011 Keywords: Arden Syntax Java Bytecode Decision support Knowledge based systems Compiler Service oriented architecture a b s t r a c t Patient empowerment might be one key to reduce the pressure on health care systems challenged by the expected demographic changes. Knowledge based systems can, in com- bination with automated sensor measurements, improve the patients’ ability to review their state of health and make informed decisions. The Arden Syntax as a standardized language to represent medical knowledge can be used to express the corresponding decision rules. In this paper we introduce Arden2ByteCode, a newly developed open source compiler for the Arden Syntax. Arden2ByteCode runs on Java Virtual Machines (JVM) and translates Arden Syntax directly to Java Bytecode (JBC) executable on JVMs. Arden2ByteCode eas- ily integrates into service oriented architectures, like the Open Services Gateway Initiative (OSGi) platform. Apart from an evaluation of compilation performance and execution times, Arden2ByteCode was integrated into an existing knowledge supported exercise training system and recorded training sessions have been used to check the implementation. © 2011 Elsevier Ireland Ltd. All rights reserved. 1. Introduction Recently sensor enhanced health information systems have been proposed as one among many measures to alleviate the effects of demographic change [1]. Utilizing repeatedly or continuously performed measurements by sensor systems detailed information of an individual’s state of health can be produced [2]. The information hidden in the gathered data could empower each individual to live a healthier life [3], to detect developing diseases earlier [4], and to effectively ∗ Corresponding author. Tel.: +49 531 391 2126; fax: +49 531 391 9502. E-mail address: Klaus-Hendrik.Wolf@plri.de (K.-H. Wolf). 1 Present address: Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany. manage chronic diseases [5]. Giving back the patient more responsibility for his own health is expected to have a positive impact on health care costs. One limiting factor is the patient’s lack of knowledge to interpret measurements in the context of their personal health to make informed decisions. Therefore, they need to be assisted. Personalized decision support systems could help to bridge this gap. The goal of our research was to develop components for decision support systems (DSSs) that can be seamlessly inte- grated into sensor enhanced health information systems. The 0169-2607/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.cmpb.2011.11.003