Checking coding completeness by mining discharge summaries.

Incomplete coding is a known problem in hospital information systems. In order to detect non-coded secondary diseases we developed a text classification system which scans discharge summaries for drug names. Using a drug knowledge base in which drug names are linked to sets of ICD-10 codes, the system selects those documents in which a drug name occurs that is not justified by any ICD-10 code within the corresponding record in the patient database. Treatment episodes with missing codes for diabetes mellitus, Parkinson's disease, and asthma/COPD were subject to investigation in a large German university hospital. The precision of the method was 79%, 14%, and 45% respectively, roughly estimated recall values amounted to 43%, 70%, and 36%. Based on these data we predict roughly 716 non-coded diabetes cases, 13 non-coded Parkinson cases, and 420 non-coded asthma/COPD cases among 34,865 treatment episodes.

@neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services.

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

Knowledge environments representing molecular entities for the virtual physiological human.

In essence, the virtual physiological human (VPH) is a multiscale representation of human physiology spanning from the molecular level via cellular processes and multicellular organization of tissues to complex organ function. The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly. Here, we describe methods and strategies to generate knowledge environments representing molecular entities that can be used for modelling the molecular scale of the VPH. Our strategy to generate knowledge environments representing molecular entities is based on the combination of information extraction from scientific text and the integration of information from biomolecular databases. We introduce @neuLink, a first prototype of an automatically generated, disease-specific knowledge environment combining biomolecular, chemical, genetic and medical information. Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.

The @neurIST ontology of intracranial aneurysms: providing terminological services for an integrated IT infrastructure.

The @neurIST ontology is currently under development within the scope of the European project @neurIST intended to serve as a module in a complex architecture aiming at providing a better understanding and management of intracranial aneurysms and subarachnoid hemorrhages. Due to the integrative structure of the project the ontology needs to represent entities from various disciplines on a large spatial and temporal scale. Initial term acquisition was performed by exploiting a database scaffold, literature analysis and communications with domain experts. The ontology design is based on the DOLCE upper ontology and other existing domain ontologies were linked or partly included whenever appropriate (e.g., the FMA for anatomical entities and the UMLS for definitions and lexical information). About 2300 predominantly medical entities were represented but also a multitude of biomolecular, epidemiological, and hemodynamic entities. The usage of the ontology in the project comprises terminological control, text mining, annotation, and data mediation.

Comparison of ICHI and CCAM basic coding system.

While diagnoses are coded by ICD across the world, there is no universally accepted coding system for procedures. In many countries there exists not even a local classification for medical procedures. As a possible solution the International Classification of Health Interventions (ICHI) has been proposed as a common denominator for an international procedure classification. We alternatively postulate a multiaxial framework for procedure classification following the French Classification Commune des Actes Médicaux (CCAM) for the generation of a procedure shortlist. We compared ICHI and CCAM Basic Coding System focusing on the appropriateness of both systems for supporting the comparability of procedure data. Considering the ongoing standardization of health terminologies and classifications, we strongly recommend to improve the ICHI structure, capitalizing on the benefits of the CCAM architecture.