Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

How to Distinguish Parthood from Location in Bio-Ontologies

The pivotal role of the relation part-of in the description of living organisms is widely acknowledged. Organisms are open systems, which means that in contradistinction to mechanical artifacts they are characterized by a continuous flow and exchange of matter. A closer analysis of the spatial relations in biological organisms reveals that the decision as to whether a given particular is part-of a second particular or whether it is only contained-in the second particular is often controversial. We here propose a rule-based approach which allows us to decide on the basis of well-defined criteria which of the two relations holds between two anatomical objects, given that one spatially includes the other. We discuss the advantages and limitations of this approach, using concrete examples from human anatomy

Semantic processing of medical data

Medical images increase in quality and quantity: More and more detailed image content can be represented on the pixel level, and increasing amounts of medical images are produced in the context of clinical diagnosis. Technological solutions are needed to enhance existing clinical IT solutions helping clinicians to access and use medical images optimally. Within MEDICO, we developed methods and tools (a) to parse and describe the content of medical images, (b) to extract and annotate the related information from radiology reports, and (c) to provide and manage medical ontologies as a common language for labeling and integrating the various information entities

Comparing the DebugIT dashboards to national surveillance systems

International audiencepas de résum

Natural Language Processing in Diagnostic Texts from Nephropathology

Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination

DebugIT: Ontology-mediated layered Data Integration for real-time Antibiotics Resistance Surveillance

Antibiotics resistance poses a significant problem in today’s hospital care. Although large amounts of resistance data are gathered locally, they cannot be compared globally due to format and access diversity. We present an ontology-based integration approach serving an EU project in making antibiotics resistance data semantically and geographically interoperable. We particularly focus on EU-wide clinical data integration for real-time antibiotic resistance surveillance. The data semantics is formalized by multiple layers of terminology-bound description logic ontologies. Local database-to-RDF (D2R) converters, normalizers and data wrapper ontologies render hospital data accessible to SPARQL queries, which populate a mediator layer. This semiformal data is then integrated and rendered comparable via formal OWL domain ontologies and rule-driven reasoning applications. The presented integration layer enables clinical data miners to query over multiple hospitals which behave like one homogeneous ‘virtual clinical information system’. We show how cross-site querying can be achieved across borders, languages and different data models. Aside the drawbacks, we elaborate on the unique advantages over comparable previous efforts, i.e. tackling real-time data access and scalability

Literature Review on Patient-Friendly Documentation Systems

Åhlfeldt H, Borin L, Grabar N, et al. Literature Review on Patient-Friendly Documentation Systems. Milton Keynes, UK: Centre for Research in Computing, The Open University; 2006.This literature review forms a deliverable in the European Network of Excellence on Semantic Interoperability and Data Mining in Biomedicine. More specifically, it is part of a work package (WP27) which aims to develop and evaluate generic methods and tools for assisting patients to understand their health and healthcare by generating patient-friendly readable texts that paraphrase the content of their electronic health records. We have reviewed the literature in topics that we consider to be relevant to this work package. When appropriate, we cover variations in conditions in the four countries of the collaborating research groups (France, Germany, Sweden and the UK) and we cover corpora, tools and language technologies for the European languages of interest to these groups