Implementing electronic scales to support standardized phenotypic data collectionthe case of the Scale for the Assessment and Rating of Ataxia (SARA)

Resumo

In the clinical domain, phenotypic abnormalities are defined as alterations in normal morphology, physiology, or behavior. In rare genetic diseases, computational representation of phenotypic abnormalities is crucial to improve the interpretation of the genetic tests. Unlike genomic technology, collecting and analyzing phenotype data is not usually conducted following a standardized process. Rating scales represent an important resource for standardized data collection, especially in neurology. Representing rating scales using clinical information archetypes promotes computational data standardization. However, phenotypic descriptions arise from clinical interpretation of the collected data. Hence, their computational representation requires facilities for exploiting reasoning on clinical archetypes, which is a challenge nowadays. The main objective of this doctoral thesis was to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. In order to address the objective, we combined the best performances from clinical archetypes, guidelines and ontologies for developing an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. A scaled-down version of the Human Phenotype Ontology was automatically extracted and used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by SCA36. Our results reveal a substantial degree of agreement between the results achieved by the prototype and human experts, confirming that the combination of archetypes, ontologies and guidelines is a good solution to automate the extraction of relevant phenotypic knowledge from plain scores of rating scales.