Semi-Automated Approach to Retrieve SNOMED CT Hierarchy of Clinical Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. In addition, hierarchy retrieval of clinical concepts increases the challenges for the user.

Semi-Automated Approach to Map Clinical Concepts to SNOMED CT Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. Terminology Servers provide an interface, which can be used to automate the process of retrieving data.

Mapping of OpenEHR Archetypes to FHIR Resources in Use Case Oncology.

Unambiguous data exchange among healthcare systems is essential for error-free reporting and improved patient care. Mapping of different standards plays a crucial role in making different systems communicate with each other and have an efficient healthcare systems. This work focuses on exploring the possibilities of semantic interoperability between two widely used clinical modelling standards, OpenEHR and FHIR (Fast Healthcare Interoperability Resources).

An OpenEHR Template with the Integrated German LOINC Terms.

An OpenEHR template based on LOINC terms in German language (LOINC-DE) has been created for the structured clinical data capture. The resulting template includes all terms available in LOINC-DE, which can be selected from the drop-down menu for clinical data capture. The template can be used as an independent laboratory form or it can be customized for local needs.

Semi-Automated Approach to Populate any FHIR Server via POST Method.

FHIR (Fast Healthcare Interoperability Resources) is a specification for exchanging healthcare data electronically. We provide a relatively easy way to populate any FHIR server by using a workflow. A dataset of 25 FHIR JSON files with resource type Bundles, synthetically generated by using Synthea, has been tested for the population of the Vonk Server.

Perspectives and Obstacles for Transforming Terminologies into FHIR CodeSystems Exemplified by Alpha-ID.

The terminology services, defined as part of the emerging FHIR standard, yield a promising approach to finally achieve a common handling of coding systems needed for semantic interoperability. As a precondition, legacy terminology data must be transformed into FHIR-compatible resources whereby varying source formats make a manual case-by-case solution impracticable. In this work, the practicability of using CSIRO's Ontoserver and the related Snapper tool as support of the transformation process were evaluated by applying them to the German Alpha-ID terminology.

Agent based modeling of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis.

Background: Multiple sclerosis (MS) is a disease of central nervous system that causes the removal of fatty myelin sheath from axons of the brain and spinal cord. Autoimmunity plays an important role in this pathology outcome and body's own immune system attacks on the myelin sheath causing the damage. The etiology of the disease is partially understood and the response to treatment cannot easily be predicted.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

Purpose: The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes.

Methods: Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports.

Biggest challenges in bioinformatics.

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the 'Biggest Challenges in Bioinformatics' in a 'World Café' style event.

Extraction of potential adverse drug events from medical case reports.

: The sheer amount of information about potential adverse drug events published in medical case reports pose major challenges for drug safety experts to perform timely monitoring. Efficient strategies for identification and extraction of information about potential adverse drug events from free-text resources are needed to support pharmacovigilance research and pharmaceutical decision making. Therefore, this work focusses on the adaptation of a machine learning-based system for the identification and extraction of potential adverse drug event relations from MEDLINE case reports.

Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports.

A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations.