The added value of text from Dutch general practitioner notes in predictive modeling

Objective:This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.Materials and methods:We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems.Results:On average, over all the different text representations and prediction algorithms, models only using text data performed better or similar to models using structured data alone in 2 prediction tasks. Additionally, in these 2 tasks, the combination of structured and text data outperformed models using structured or text data alone. No large performance differences were found between the different text representations and prediction algorithms.Discussion:Our findings indicate that the use of unstructured data alone can result in well-performing prediction models for some clinical prediction problems. Furthermore, the performance improvement achieved by combining structured and text data highlights the added value. Additionally, we demonstrate the significance of clinical natural language processing research in languages other than English and the possibility of validating text-based prediction models across various EHR systems.Conclusion:Our study highlights the potential benefits of incorporating unstructured data in clinical prediction models in a GP setting. Although the added value of unstructured data may vary depending on the specific prediction task, our findings suggest that it has the potential to enhance patient care

DMARD-free remission as novel treatment target in rheumatoid arthritis: A systematic literature review of achievability and sustainability

OBJECTIVES: Although current treatment guidelines for rheumatoid arthritis (RA) suggest tapering disease-modifying anti-rheumatic drugs (DMARDs), it is unclear whether DMARD-free remission (DFR) is an achievable and sustainable outcome. Therefore, we systematically reviewed the literature to determine the prevalence and sustainability of DFR and evaluated potential predictors for DFR. METHODS: A systematic literature search was performed in March 2019 in multiple databases. All clinical trials and observational studies reporting on discontinuation of DMARDs in RA patients in remission were included. Our quality assessment included a general assessment and assessment of

Alignment of the UMLS semantic network with BioTop: Methodology and assessment

Motivation: For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop. Methods: The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability. Results: The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up s

Extraction of chemical-induced diseases using prior knowledge and textual information

We describe our approach to the chemical-disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: Automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached an F-score of 0.757. For CID, the system achieved an F-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improved F-scores (0.828 for DNER and 0.602 for CID)

Exploring Large Document Repositories with RDF Technology: The DOPE Project

This thesaurus-based search system uses automatic indexing, RDF-based querying, and concept-based visualization of results to support exploration of large online document repositories

Bewijs voor effectiviteit van Comprehensive Geriatric Assessment in de thuissituatie nog mager: een literatuurreview

Achtergrond De meeste ouderen vinden het belangrijk zo lang mogelijk zelfstandig te blijven functioneren. Gerichte opsporing en behandeling van factoren die de functionele zelfstandigheid bedreigen, middels een comprehensive geriatric assessment (CGA), kan het zelfstandig functioneren mogelijk bevorderen. Dit artikel doet verslag van een literatuurstudie naar het effect van comprehensive geriatric assessment in de thuissituatie. Methode In Pubmed (1977–2012) is gezocht naar RCT’s die de effectiviteit van multidisciplinaire, multidimensionele CGA bij ouderen in de thuissituatie onderzocht hebben. Er werden data geëxtraheerd over effectiviteit, kosten en factoren die het effect van het CGA positief of negatief hebben beïnvloed. Resultaten Negen RCT’s werden geïncludeerd. Op één studie na was de kwaliteit matig tot goed. In drie van de zes studies naar functionele status en in twee van de vier studies naar kwaliteit van leven werd een positief effect gevonden. Geen effect werd gevonden op het aantal ziekenhuisopnames en verpleeghuisopnames of op mortaliteit. De meeste studies lieten een stijging zien van de totale gezondheidszorgkosten. Conclusie CGA thuis heeft een beperkt gunstig effect op functionele status en kwaliteit van leven. Er zijn aanwijzingen dat CGA thuis het meeste effect heeft bij relatief goed functionerende ouderen

Automated extraction of potential migraine biomarkers using a semantic graph

Problem Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers. Method We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance. Results Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974. Discussion Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers

Factors that influence biological survival in rheumatoid arthritis: results of a real-world academic cohort from the Netherlands

We aim to explore real-world biological survival stratified for discontinuation reason and determine its influenceability in rheumatoid arthritis (RA) patients. Data from the local pharmacy database and patient records of a university hospital in th