An architecture for an integrated medical workstation : its realization and evaluation

This study describes the development of the HERMES integrated medical workstation for the support of patient care and clinical data analysis. Tbis development proceeded in two steps. First, a prototype integrated workstation was developed for the limited domain of support for clinical data analysis. Second, insight resulting from experience with the design and implementation of the prototype, and from the outcome of its formal user evaluation were used as input to design the new HERMES architecture, also intended to encompass the support of patient care. HERMES offers a solution for the urgent problem in medical informatics of integrating different applications on different hosts. Our approach combines the client-server paradigm with a graphical user interface to provide user-friendly access to the clinician. Its application domain includes both patient care and clinical data analysis. In this introductory chapter, we will briefly introduce the idea of providing integrated computer support to the clinician and the recent progress made in computer science that enables this novel approach to workstation integration

Training text chunkers on a silver standard corpus: Can silver replace gold?

Background: To train chunkers in recognizing noun phrases and verb phrases in biomedical text, an annotated corpus is required. The creation of gold standard corpora (GSCs), however, is expensive and time-consuming. GSCs therefore tend to be small and to focus on specific subdomains, which limits their usefulness. We investigated the use of a silver standard corpus (SSC) that is automatically generated by combining the outputs of multiple chunking systems. We explored two use scenarios: one in which chunkers are trained on an SSC in a new domain for which a GSC is not available, and one in which chunkers are trained on an available, although small GSC but supplemented with an SSC.Results: We have tested the two scenarios using three chunkers, Lingpipe, OpenNLP, and Yamcha, and two different corpora, GENIA and PennBioIE. For the first scenario, we showed that the systems trained for noun-phrase recognition on the SSC in one domain performed 2.7-3.1 percenta

A prototype integrated medical workstation environment

Abstract In this paper the requirements, design, and implementation of a prototype integrated medical workstation environment are outlined. The aim of the workstation is to provide user-friendly, task-oriented support for clinicians, based on existing software and data. The prototype project has been started to investigate the technical possibilities of graphical user-interfaces, network technology, client-server approaches, and software encapsulation. Experience with the prototype encouraged discussion on both the limitations and the essential features for an integrated medical workstation

Discovering information from an integrated graph database

The information explosion in science has become a different problem, not the sheer amount per se, but the multiplicity and heterogeneity of massive sets of data sources. Relations mined from these heterogeneous sources, namely texts, database records, and ontologies have been mapped to Resource Description Framework (RDF) triples in an integrated database. The subject and object resources are expressed as references to concepts in a biomedical ontology consisting of the Unified Medical Language System (UMLS), UniProt and EntrezGene and for the predicate resource to a predicate thesaurus. All RDF triples have been stored in a graph database, including provenance. For evaluation we used an actual formal PRISMA literature study identifying 61 cerebral spinal fluid biomarkers and 200 blood biomarkers for migraine. These biomarkers sets could be retrieved with weighted mean average precision values of 0.32 and 0.59, respectively, and can be used as a first reference for further refinements

Erasmus MC at CLEF eHealth 2016: Concept recognition and coding in French texts

We participated in task 2 of the CLEF eHealth 2016 chal-lenge. Two subtasks were addressed: entity recognition and normalization in a corpus of French drug labels and Medline titles, and ICD-10 coding of French death certificates. For both subtasks we used a dictionary-based approach. For entity recognition and normalization, we used Peregrine, our open-source indexing engine, with a dictionary based on French terms in the Unified Medical Language System (UMLS) supplemented with English UMLS terms that were translated into French with automatic translators. For ICD-10 coding, we used the Solr text tagger, together with one of two ICD-10 terminologies derived from the task training ma-terial. To reduce the number of false-positive detections, we implemented several post-processing steps. On the challenge test set, our best system obtained F-scores of 0.702 and 0.651 fo

Knowledge-based extraction of adverse drug events from biomedical text

Background: Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledg

Mining microarray datasets aided by knowledge stored in literature

DNA microarray technology produces large amounts of data. For data mining of these datasets, background information on genes can be helpful. Unfortunately most information is stored in free text. Here, we present an approach to use this information for DNA microarray data mining

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)

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

Ambiguity of human gene symbols in LocusLink and MEDLINE: creating an inventory and a disambiguation test collection

Genes are discovered almost on a daily basis and new names have to be found. Although there are guidelines for gene nomenclature, the naming process is highly creative. Human genes are often named with a gene symbol and a longer, more descriptive term; the short form is very often an abbreviation of the long form. Abbreviations in biomedical language are highly ambiguous, i.e., one gene symbol often refers to more than one gene.Using an existing abbreviation expansion algorithm,we explore MEDLINE for the use of human gene symbols derived from LocusLink. It turns out that just over 40% of these symbols occur in MEDLINE, however, many of these occurrences are not related to genes. Along the process of making an inventory, a disambiguation test collection is constructed automatically