Karl Anton Fröschl

On analyzing process compliance in skin cancer treatment: an experience report from the evidence-based medical compliance cluster (EBMC 2 )

Process mining has proven itself as a promising analysis technique for processes in the health care domain. The goal of the EBMC2 project is to analyze skin cancer treatment processes regarding their compliance with relevant guidelines. For this, first of all, the actual treatment processes have to be discovered from the available data sources. In general, the L* life cycle model has been suggested as structured methodology for process mining projects. In this experience paper, we describe the challenges and lessons learned when realizing the L* life cycle model in the EBMC2 context. Specifically, we provide and discuss different approaches to empower data of low maturity levels, i.e., data that is not already available in temporally ordered event logs, including a prototype for structured data acquisition. Further, first results on how process mining techniques can be utilized for data screening are presented.

Assessing medical treatment compliance based on formal process modeling

The formalization and analysis of medical guidelines play an essential role in clinical practice nowadays. Due to their inexorably generic nature such guidelines leave room for different interpretation and implementation. Hence, it is desirable to understand this variability and its implications for patient treatment in practice. In this paper we propose an approach for comparing guideline-based treatment processes with empirical treatment processes. The methodology combines ideas from workflow modeling, process simulation, process mining, and statistical methods of evidence-based medicine. The applicability of the approach is illustrated based on the Cutaneous Melanoma use case.

Semantic Online Tourism Market Monitoring

SEMAMO (SEmantic MArket MOnitoring) is a research project seeking to make use of the increasingly growing information available at Web-based sales and marketing channels for continuous market research. Assuming that online channels indeed mirror salient market developments faithfully, SEMAMO implements a nearly fully automatic adaptive data capture and analysis process delivering customer-defined market reports on demand. The paper describes the SEMAMO prototype implementation and exemplifies the functionality and utility of the approach in the domain of e-tourism, with a focus on the type of reports and visualisations the software, albeit not completely finished yet, can already deliver based on real-world data. Additionally, the role of formal domain description in SEMAMO is emphasized.

A Method for Analyzing Time Series Data in Process Mining: Application and Extension of Decision Point Analysis

The majority of process mining techniques focuses on control flow. Decision Point Analysis (DPA) exploits additional data attachments within log files to determine attributes decisive for branching of process paths within discovered process models. DPA considers only single attribute values. However, in many applications, the process environment provides additional data in form of consecutive measurement values such as blood pressure or container temperature. We introduce the DPATS method as an iterative process for exploiting time series data by combining process and data mining techniques. The latter ranges from visual mining to temporal data mining techniques such as dynamic time warping and response feature analysis. The method also offers different approaches for incorporating time series data into log files in order to enable existing process mining techniques to be applied. Finally, we provide the simulation environment DPATSSim to produce log files and time series data. The DPATS method is evaluated based on application scenarios from the logistics and medical domain.

EBMC2 – der Evidence-Based Medical Compliance Cluster

Um medizinische Behandlungsprozesse mit evidenzbasierten Leitlinien am Beispiel des kutanen Melanoms zu analysieren, wurde 2010 der Evidence-Based Medical Compliance Cluster (EBMC2) gebildet. Dieser besteht aus medizinischen Experten und Spezialisten in den Bereichen der medizinischen Informatik, der Informationssysteme und der Workflow Management Technologie.