A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Automated generation of decision-tree models for the economic assessment of interventions for rare diseases using the RaDiOS ontology

OBJECTIVE

The development of decision models to assess interventions for rare diseases require huge efforts from research groups, especially regarding collecting and synthesizing the knowledge to parameterize the model. This article presents a method to reuse the knowledge collected in an ontology to automatically generate decision tree models for different contexts and interventions.

MATERIAL AND METHODS

We updated the reference ontology (RaDiOS) to include more knowledge required to generate a model. We implemented a transformation tool (RaDiOS-MTT) that uses the knowledge stored in RaDiOS to automatically generate decision trees for the economic assessment of interventions on rare diseases.

RESULTS

We used a case study to illustrate the potential of the tool, and automatically generate a decision tree that reproduces an actual study on newborn screening for profound biotinidase deficiency.

CONCLUSIONS

RaDiOS-MTT allows research groups to reuse the evidence collected, and thus speeding up the development of health economics assessments for interventions on rare diseases.

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

The MalaCards human disease database (http://www.malacards.org/) is an integrated compendium of annotated diseases mined from 68 data sources. MalaCards has a web card for each of ∼20 000 disease entries, in six global categories. It portrays a broad array of annotation topics in 15 sections, including Summaries, Symptoms, Anatomical Context, Drugs, Genetic Tests, Variations and Publications. The Aliases and Classifications section reflects an algorithm for disease name integration across often-conflicting sources, providing effective annotation consolidation. A central feature is a balanced Genes section, with scores reflecting the strength of disease-gene associations. This is accompanied by other gene-related disease information such as pathways, mouse phenotypes and GO-terms, stemming from MalaCards’ affiliation with the GeneCards Suite of databases. MalaCards’ capacity to inter-link information from complementary sources, along with its elaborate search function, relational database infrastructure and convenient data dumps, allows it to tackle its rich disease annotation landscape, and facilitates systems analyses and genome sequence interpretation. MalaCards adopts a ‘flat’ disease-card approach, but each card is mapped to popular hierarchical ontologies (e.g. International Classification of Diseases, Human Phenotype Ontology and Unified Medical Language System) and also contains information about multi-level relations among diseases, thereby providing an optimal tool for disease representation and scrutiny.