Dr. Zhengxing Huang of Zhejiang University (Hangzhou, China)
Predictive monitoring of clinical pathways.
Accurate and timely monitoring, as a key aspect of clinical pathway (CP) management, provides crucial information to medical staff and hospital managers for determining the efficient medical service delivered to individual patients, and for promptly handling unusual treatment behaviors in CPs. In many applications, CP monitoring is performed in a reactive manner, e.g., variant treatment events are detected only after they have occurred in CPs. Alternatively, this study systematically presents a learning framework for predictive monitoring of CPs. The proposed framework is composed of both offline analysis and online monitoring phases. In the offline phase, a particular probabilistic topic model, i.e., treatment pattern model (TPM), is generated from electronic medical records to describe essential/critical medical behaviors of CPs. Using TPM-based measures as a descriptive vocabulary, online monitoring of CPs can be provided for ongoing patient-care journeys. Specifically, two typical predictive monitoring services, i.e., unusual treatment event prediction and clinical outcome prediction, are presented to illustrate how the potential of the proposed framework can be exploited to provide online monitoring services from both internal and external perspectives of CPs. Extensive evaluation on a real clinical data-set, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based CP management in a predictive manner.
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A bioinformatic approach to mesothelioma therapeutics: from ADAM to TRAP
Arginine deprivation is a novel antimetabolite strategy for the treatment of arginine-dependent cancers that exploits differential expression and regulation of key urea cycle enzymes. Several studies have focused on inactivation of argininosuccinate synthetase 1 (ASS1) in a range of malignancies, including melanoma, hepatocellular carcinoma, and mesothelioma. Promoter methylation, in particular, has been identified as a mechanism for loss of the tumor suppressor role of ASS1 leading to tumoral dependence on exogenous arginine. Clinical trials of several arginine depletors are ongoing, including pegylated arginine deiminase (ADI-PEG20, Polaris Group, US) and bioengineered forms of human arginase. The challenge will be to identify tumors sensitive to arginine depletors, and integrate these agents into multimodality drug regimens using predictive biomarkers. Here, we have applied a bioinformatic approach to identify tractable pathways with ADI-PEG20 in the treatment of patients with mesothelioma. Recently, our phase 2 study of ADI-PEG20 in mesothelioma (ADAM) completed accrual and we are now launching a phase I combinatorial trial (TRAP) in the UK based on our bioinformatics studies.
Dr Peter Szlosarek (MBBS BSc MRCP PhD) is a Clinical Senior Lecturer at the Barts Cancer Institute, and Cancer Physician at St. Bartholomew’s Hospital, London. He studied Medicine and Pharmacology at King’s College, London and then specialised in Medical Oncology completing a PhD on the links between TNF-a, inflammation and cancer at the University of London. His clinical and lab research interests are in metabolic approaches to cancer therapy, particularly the role of arginine deprivation therapy in arginine-dependent cancers. This has led to clinical trials of the arginine-depleting agent ADI-PEG20 (Polaris Group, US) in mesothelioma (CTAAC grant) and small cell lung cancer, the latter a collaboration with the Ludwig Institute for Cancer Research in New York, US. He is funded by several grant bodies including Cancer Research UK, Barts and The London Charity, Medical Research Council and the British Lung Foundation. He maintains a research-orientated clinical practice at Barts in thoracic and cutaneous malignancy and is a member of the Royal College of Physicians, the Association of Cancer Physicians, the EORTC, AACR and ASCO.
Regulatory Compliance Management (RCM) is widely recognized as one of the main challenges still to be efficiently dealt with in Enterprise Models (EMs). In the discipline of Business Process Management (BPM) in particular, which plays a central role in modern management of enterprises, compliance is considered as an important driver of the efficiency, reliability and market value of companies. It consists of ensuring that enterprise systems behave according to some guidance provided in the form of regulations.
Existing approaches to RCM tackle this issue from two different perspectives: methodological and formal. The first category of a approaches is widely used in the industry and proposes several processes based on controls for compliance audit and governance. The second category of approaches seeks to construct complex formal languages and reasoning engines for automatically deciding on the state of compliance of a business process, but remains hardly accessible to practitioners who are not trained in formal methods. This work provides an approach for modeling and checking of regulatory compliance that profits from the power of complex formal languages and is specifically targeted at practitioners.
For this purpose, we introduce CoReL, a visual domain-specific modeling language for representing compliance requirements. The main objective behind CoReL is to bring the task of compliance modeling to the business user level where it belongs. CoReL allows to leverage business process compliance modeling and checking, enhancing it with regard to, user-friendliness and coverage of various enterprise artifacts, as well as multiple types of regulatory constraints. Both informal and formal semantics of CoReL are introduced and its use for modeling and checking compliance regulations is shown on an example.
Dr. El Kharbili’s core fields of research are Enterprise Architectures/Business Process Management and Model Driven Engineering. His main area of research covers the development of procedures, languages and tools for the modelling, automated verification and analysis of regulatory compliance in information systems as well as the governance thereof.
Dr. El Kharbili is a graduate both the Grenoble Institute of Technology (France) and the Karlsruhe Institute of Technology (Germany), and holds a M.Sc. from each university. He holds a Ph.D. in computer science from the University of Luxembourg and another Ph.D. degree also in computer science from the University of Osnabrueck (Germany). In his Ph.D. thesis he developed methods and languages for enterprise regulatory compliance management of enterprise models and business processes by applying and extending techniques form formal methods, security policies and software language engineering.
Prior to that he worked in the software industry and industrial research at both the IDS Scheer AG (inventors of the ARIS framework) and SAP AG. Dr. El Kharbili’s core fields of research are Enterprise Architectures/Business Process Management and Model Driven Engineering.
If you would like to know more, you can find a more complete BIO on: http://theintelligententerprise.blogspot.com.au/2010/11/bio.html
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I completed my BSc and MSc degrees in Computer Engineering and I had my PhD
(2000-2006) in Information Systems from the Middle East Technical University of
Turkey. During my studentship, I had participated in several projects including business
process modeling and requirements elicitation, model-based assessment and
improvement of software processes, and software quality attributes specification and
evaluation. The title of my PhD thesis was “An Assessment Approach for the
Applicability of Statistical Process Control for Software Processes”.
After completing my PhD, I started as a Senior Lecturer in the Computer Engineering
Department of Hacettepe University, Turkey; and currently work as an Assistant
Professor in the same department. The courses I teach include Software Engineering,
Software Quality Management, Software Metrics, Personal Software Process, and
Advanced UML Modeling. I pursue my research studies with a focus on software quality, software process management, and software engineering standards with more than a dozen graduate students. I have been the Deputy Manager of the Informatics Institute of the university since February 2013 and am also the founder of Software Engineering Master Program under this institute.
Since I have a substantial experience on model-based assessment and improvement of
software processes, I had a thought that this might make a contribution to the area of
Business Process Management. Dr. Oktay Türetken and I have formed a research
project entitled “Development of a Self-Assessment Approach for Business Process
Maturity” and got a grant for this from the Scientific Research Council of Turkey. I am
therefore happy to visit TU/e for a year starting from September 2013 to work with Dr.
Türetken and meet with the IS staff and their research, and declare my appreciation to
Prof. Paul W.P.J. Grefen for his harborous welcome.
Speaker: Prof.Dr. Adnan Yazıcı, Computer Engineering Department, Middle East Technical University, Ankara-Turkey
Title: Modeling and Management of Multimedia Data
Abstract: Due to the developments in information and communication technologies, the volume and usage of multimedia data increases rapidly and extracting semantic information and querying them has become an important requirement in our daily lives. Conceptually modeling multimedia information, extracting semantic information from multimedia data, storing and querying multimedia content in and from a multimedia database system and accessing them efficiently from a database are some of the important research topics in multimedia information retrieval area. In this talk I will talk about the research on multimedia information/data management that has been going on in Multimedia Database Laboratory, established in Computer Engineering Department at Middle East Technical University.
Biography: Prof. Dr. Adnan Yazıcı is chairman of Dept. of Computer Engineering, METU. He received his Ph.D. in Computer Science from the Department of EECS at Tulane University, USA, in 1991, where he also has been a visiting professor between 1998 and 2000. His current research interests include intelligent database systems, fuzzy database modeling, spatio-temporal databases, multimedia and video databases, and wireless multimedia sensor networks. Prof.Dr. Adnan Yazıcı has published more than 180 international technical papers and co-authored two books, which are titled Fuzzy Database Modeling (by Springer) and Uncertainty Approaches for Spatial Data Modeling and Processing: a Decision Support Perspective (by Springer). He is a senior member of IEEE and has received IBM Faculty Award for 2011 and Young Investigator Award bestowed by the Parlar Foundation, for the year 2001. Dr. Yazıcı was a Conference Co-Chair of the 23rd IEEE International Conference on Data Engineering (ICDE) in 2007 and a Conference Co-Chair of the 38th Very Large Data Bases (VLDB 2012) conference. He was also the Program Committee Chair of the 18th International Symposium of Computer and Information Sciences (ISCIS 2003), one of the Program Committee co-chairs of ISCIS 2010 and Flexible Query Answering Systems (FQAS 2011). He is the director of Multimedia Database Lab. in the Computer Engineering Department at METU.
Dr. Llanos Cuenca González is an Assistant Professor in Enterprise Management and Management Information Systems at the Polytechnic University of Valencia (UPV) in Spain. She is a computer science engineer and received her Ph.D from the UPV. She is member of Research Centre on Production Management and Engineering (CIGIP). She has participated as a researcher on several Spanish and European projects. In these projects, she worked on issues related to Production Management, Supply Chain, Enterprise Modelling and the use of ICT in industrial enterprises. At this moment her research lines focus on Interoperability, Enterprise Architectures, Strategic Alignment and the use of ICT in these areas. As result of this research, she has published several papers in international conferences and journals.
Duration visit IS-group: Jan 8 – Feb 10 2013
Alignment of business strategy and information technology strategy from an enterprise engineering perspective
Although potentially, incorporating information systems and information technology (IT) into organizations offers significant returns, it involves considerable risks. Besides, these risks increase when a strategic plan for this incorporation is not provided. Enterprise engineering (EE) facilitates formal dialog in enterprise design.
Business and IT alignment (BITA) enables and drives the IT strategy jointly with the business strategy. The IT mission, objectives and plans support, and are supported by the business mission, objectives and plans. It can be addressed by these two questions: (1) how is IT aligned with business and; (2) how should or could business be aligned with IT. This alignment process evolves into a relationship where IT and other business functions adapt their strategies together. The strategic alignment model, proposed by Henderson and Venkatraman, is composed of four quadrants that consist of three components each one. These twelve components define what each quadrant is as far as alignment is concerned. All the components working together determine the extent of alignment for the company being assessed.
Enterprise architectures (EA) enable alignment. Business and information systems can be modelled together in a common organizational framework. In this case, business and IT domains are integrated and are visible in a common framework. Every EA contain views within their frameworks; however, life cycles, building blocks and how the building blocks fit together are not defined by all of them, thus making the alignment between components difficult.
Memetic Algorithms for Ontology Alignment
Born primarily as means to model knowledge, ontologies have successfully been exploited to enable knowledge exchange among people, organizations and software agents. However, because of strong subjectivity of ontology modeling, a matching process is necessary in order to lead ontologies into mutual agreement and obtain the relative alignment, i.e., the set of correspondences among them. The topic of this presentation is to propose the application of an emergent class of evolutionary
algorithms, named Memetic Algorithms, to perform an automatic matching process. As shown in the performed experiments, the memetic approaches result suitable for solving ontology alignment problem.