Workshop Program

8:45 - 9:00
Opening (Chair: Silvia Miksch and David Riaño)
9:00 - 10:15
Modeling Clinical Guidelines (Chair: David Riaño)
9:00 - 9:25
Yuanlin Zhang and Zhizheng Zhang
Preliminary Result on Finding Treatments for Patients with Comorbidity
9:25 - 9:50
Veruska Zamborlini, Marcos Da Silveira, Cédric Pruski, Annette Ten Teije and Frank Van Harmelen
Towards a Conceptual Model for Enhancing Reasoning about Clinical Guidelines: A case‐study on Comorbidity
9:50 10:15
Szymon Wilk, Martin Michalowski, Xing Tan and Wojtek Michalowsk
Using First‐Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Int
10:15 ‐ 10:45
Coffe Break
10:45 ‐ 13:00
Exploring and Assessing Clinical Guidelines (Chair: Silvia Miksch)
10:45 11:10
Matteo Spiotta, Alessio Bottrighi, Laura Giordano and Daniele Theseider Dupre
Conformance Analysis of the Execution of Clinical Guidelines with Basic Medical Knowledge and Clinical
11:10 11:35
Zhisheng Huang, Annette Ten Teije, Frank Van Harmelen and Salah Ait‐Mokhtar
Semantic Representation of Evidence‐based Clinical Guidelines
11:35 12:00
Daniela Miao and K. Krasnow Waterman
Real Rules, Real Data ‐‐ Addressing Challenges of Reasoning HIPAA Privacy Rule in Health Information
12:00 12:15
META‐GLARE: a meta‐system for defining your own CIG system: Architecture and Acquisition
12:15 12:30
Mar Marcos, Joaquín Torres‐Sospedra and Begoña Martínez‐Salvador
Assessment of Clinical Guideline Models Based on Metrics for Business Process Models
12:30 12:45 Begoña Martínez‐Salvador, Mar Marcos and Anderson Sánchez
An Algorithm for Guideline Transformation: from BPMN to PROforma
12:45 13:00
Aisan Maghsoodi, Paul De Bra and Anca Bucur
A Process‐oriented Methodology for Modelling Cancer Treatment Trial Protocols
13:00 ‐ 14:30
14:30 ‐ 15:30
Keynote: Stefania Montani
Knowledge‐Intensive Medical Process Similarity
15:30 ‐ 16:00
Training and Learning (Chair:  Begoña Martínez‐Salvador)
15:30 15:45
Dongwen Wang, Xuan Hung Le and Amneris Luque
Development of Digital Repositories of Multimedia Learning Modules and Guideline‐Driven Interactive Case
Simulation Tools for New York State HIV/HCV/STD Clinical Education Initiative
15:45 16:00
Francis Real, David Riaño and Jose‐Ramon Alonso
Training Residents in the Application of Clinical Guidelines for Differential Diagnosis of the most Frequent
Causes of Arterial Hypertension with Decision Tables
16:00 ‐ 16:30 30 Coffe Break
16:30 ‐ 17:40
Linking Electronic Patient Records (Chair: Dongwen Wang)
16:30 16:55
David Riaño and Agusti Solanas
Exploiting the Relation between Environmental Factors and Diseases: A Case Study on COPD
16:55 17:10
Oya Beyan, Ciara Breathnach, Sandra Collins, Christophe Debruyne, Stefan Decker, Dolores Grant, Rebecca Grant
and Brian Gurrin
Towards Linked Vital Registration Data for Reconstituting Families and Creating Longitudinal Health Histories
17:10 17:25 Thomas Vetterlein and Anna Zamansky
A Logic‐Based Framework for Medical Assessment Questionnaires
17:25 17:40
Marcin Hewelt
Process Information and Guidance Systems in the Hospital
17:40 ‐ 18:00
Wrap‐Up, Conclusion, and Next Steps (Chairs: Silvia Miksch and David Riaño)

Invited Speaker

Stefania Montani
DISIT - Computer Science Institute
Università del Piamonte Orientale A. Avogadro

Title: "Knowledge-intensive medical process similarity"


Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in medical applications, where similarity quantification can be exploited in a quality assessment perspective.

In this talk, I will present a framework we are developing, which allows to:

(i) extract the "actual" process model from the available process execution traces, through process mining techniques; interestingly, it may turn out that the actual process differs from the nominal process (typically, the guideline); and

(ii) compare (mined) process models, by relying on a novel distance measure; a comparison among actual processes (which embed the characteristics of the local context of execution) may be used to answer clinical research questions about the quality of care.

Our distance measure is  knowledge-intensive, in the sense that it explicitly makes use of domain knowledge, and can be properly adapted on the basis of the available knowledge representation formalism (e.g., taxonomy vs. semantic network). We also exploit all the available mined information (e.g., temporal information about delays between activities). Additionally, our metric explicitly takes into account complex  control flow information (other than sequence).

Experimental results in the stroke management domain have shown the reliability of our metric, and its superiority with respect to other literature approaches.