Tobias Weller

Toward knowledge-based liver surgery: holistic information processing for surgical decision support

PurposeMalignant neoplasms of the liver are among the most frequent cancers worldwide. Given the diversity of options for liver cancer therapy, the choice of treatment depends on various parameters including patient condition, tumor size and location, liver function, and previous interventions. To address this issue, we present the first approach to treatment strategy planning based on holistic processing of patient-individual data, practical knowledge (i.e., case knowledge), and factual knowledge (e.g., clinical guidelines and studies).MethodsThe contributions of this paper are as follows: (1) a formalized dynamic patient model that incorporates all the heterogeneous data acquired for a specific patient in the whole course of disease treatment; (2) a concept for formalizing factual knowledge; and (3) a technical infrastructure that enables storing, accessing, and processing of heterogeneous data to support clinical decision making.ResultsOur patient model, which currently covers 602 patient-individual parameters, was successfully instantiated for 184 patients. It was sufficiently comprehensive to serve as the basis for the formalization of a total of 72 rules extracted from studies on patients with colorectal liver metastases or hepatocellular carcinoma. For a subset of 70 patients with these diagnoses, the system derived an average of

Capturing and Annotating Processes using a Collaborative Platform

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Grasping the Materializations of Practicesin Digital Humanities

37±15 assertions per patient.ConclusionThe proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.

A Semantic Approach for Process Annotation and Similarity Analysis

Analysts spend a disproportionate amount of time with financial data curation before they are able to compare company performances in an analysis. The Extensible Business Reporting Language (XBRL) for annotating financial facts is suited for automatic processing to increase information quality in financial analytics. Still, XBRL does not solve the problem of data integration as required for a holistic view on companies. Semantic Web technologies promise benefits for financial data integration, yet, existing literature lacks concrete case studies. In this paper, we present the Financial Information Observation System (FIOS) that uses Linked Data and multidimensional modelling based on the RDF Data Cube Vocabulary for accessing and representing relevant financial data. FIOS fulfils the information seeking mantra of “overview first, zoom and filter, then details on demand”, integrates yearly and quarterly balance sheets, daily stock quotes as well as company and industry background information and helps analysts creating their own analyses with Excel-like functionality.

Capturing, Annotating and Processing Practical Knowledge by Using Decision Trees

Existing standards in capturing processes concentrate on client tools. Furthermore, semantic information are often available that cannot be captured in a structured way with the proposed standard formats. In addition, processes are usually used and maintained by multiple persons. Therefore, a collaborative platform to discuss and share information about processes is valuable. In order to address the challenge of maintaining and sharing knowledge about processes, we provide a tool to capture and annotate processes using Semantic MediaWiki as a collaborative platform. We demonstrate the practical applicability of our tool by presenting a demo available in the World Wide Web.

Cognitive tools pipeline for assistance of mitral valve surgery

In the surgical domain, individual clinical experience, which is derived in large part from past clinical cases, plays an important role in the treatment decision process. Simultaneously the surgeon has to keep track of a large amount of clinical data, emerging from a number of heterogeneous systems during all phases of surgical treatment. This is complemented with the constantly growing knowledge derived from clinical studies and literature. To recall this vast amount of information at the right moment poses a growing challenge that should be supported by adequate technology. While many tools and projects aim at sharing or integrating data from various sources or even provide knowledge-based decision support - to our knowledge - no concept has been proposed that addresses the entire surgical pathway by accessing the entire information in order to provide context-aware cognitive assistance. Therefore a semantic representation and central storage of data and knowledge is a fundamental requirement. We present a semantic data infrastructure for integrating heterogeneous surgical data sources based on a common knowledge representation. A combination of the Extensible Neuroimaging Archive Toolkit (XNAT) with semantic web technologies, standardized interfaces and a common application platform enables applications to access and semantically annotate data, perform semantic reasoning and eventually create individual context-aware surgical assistance. The infrastructure meets the requirements of a cognitive surgical assistant system and has been successfully applied in various use cases. The system is based completely on free technologies and is available to the community as an open-source package.

Towards a Process Meta-Model

Recently, practice theory has been established alongside humanities and social sciences in historical research and has aligned analysis according to materializations of loosely interlinked arrangements of practices, materialities, and actors (cf. Reh, 2014). While digital humanities have proven its great capacity to deal with massive data (distant reading), the digital enhancement of qualitative approaches is lacking (cf. Drucker, 2012). This desideratum is addressed in this project which uses a semantic graph to create a linked web and offering tools for combining qualitative and quantitative analysis.

Towards a Collaborative Process Platform: Publishing Processes according to the Linked Data Principles.

Research in the area of process modeling and analysis has a long-established tradition. There are quite few formalism for capturing processes, which are also accompanied by a number of optimization approaches. We introduce a novel approach, which employs semantics, for process annotation and analysis. In particular, we distinguish between target processes and current processes. Target process models describe how a process should ideally run and define a framework for current processes, which in contrast, capture how processes actually run in real-life use cases. In some cases, current processes do not match the target process models and can even overhaul them. Therefore, one is interested in the similarity between the defined target process model and current processes. The comparisons can consider different characteristics of processes such as service quality measures and dimensions. Current solutions perform process mining methods to discover hidden structures or try to infer knowledge about processes by using specific ontologies. To this end, we propose a novel method to capture and formalize processes, employing semantics and devising strategies and similarity measures that exploit the semantic representation to calculate similarities between target and current processes. As part of the similarity analysis, we consider different service qualities and dimensions in order to determine how they influence the target process models.